What Skills Does a DevOps Engineer Need?

The top priority in DevOps is to deliver high-value features frequently through cross-team collaboration. Achieving that goal requires a mix of interpersonal (“soft”) skills and technical (“hard”) skills. Whether you are already working in DevOps or preparing to move into the role, mapping your existing strengths against this framework is a useful starting point.

1. Communication and Collaboration

DevOps dissolves the traditional wall between development and operations by bringing small teams with different objectives together around a shared goal: fast, reliable releases. That model only works when communication flows freely.

A strong DevOps engineer communicates regularly with internal stakeholders — product managers, architects, QA leads — to stay aligned on roadmaps, blocking issues and release objectives. They also need to communicate clearly with customers when support issues arise.

Collaboration goes beyond being a good teammate. Effective DevOps engineers mentor colleagues on coding standards, tool selection and delivery best practices, and actively support the team throughout sprints and iterations.

2. Mastery of the DevOps Toolchain

A DevOps engineer must understand how a diverse set of tools integrates into a coherent delivery pipeline. Knowing what each tool does — and how it interacts with everything else — is as important as knowing how to configure it. The key categories include:

• Version control — tracking changes to code and configuration (e.g., Git)
• Continuous Integration (CI) servers — automating build and test on every commit (e.g., Jenkins, GitHub Actions, GitLab CI)
• Configuration management — maintaining consistent system state (e.g., Ansible, Chef, Puppet)
• Deployment automation — reliably promoting artefacts through environments
• Containers — packaging applications for portability and consistency (e.g., Docker)
• Infrastructure orchestration — managing containerised workloads at scale (e.g., Kubernetes)
• Monitoring and analytics — observing system health and user behaviour in production
• Testing and cloud quality tools — validating behaviour at every stage of the pipeline
• Network protocols — understanding how services communicate across environments

Your responsibility is to ensure these tools work together reliably in both pre-production and production stages. Knowing which tool depends on which, and in what order they fire, is essential for a stable delivery chain.

3. Programming Skills and Mature Coding Standards

DevOps engineers are not expected to be language specialists, but they do need to be comfortable working across multiple programming and scripting languages. Common examples include Python, Ruby, Bash, Java, JavaScript and PHP — the exact mix will depend on your organisation’s stack.

These skills serve two purposes:

1. Automating the pipeline — writing scripts and tools that eliminate manual handoffs between stages.
2. Integration quality — ensuring your code integrates cleanly with code produced by other teams, often in different environments and languages.

Test automation is a particularly important coding skill for DevOps engineers. Since CI/CD pipelines depend on automated tests to gate every build, the ability to write, maintain and debug test code in multiple languages is a real differentiator. Managing complex pipeline configurations in tools such as Jenkins requires discipline and a thorough end-to-end understanding of how changes propagate through the system.

4. Syncing with QA Teams

DevOps engineers do not work in isolation from quality assurance. To hit sprint goals and release dates consistently, they need continuous visibility into QA activities. Specifically, a DevOps engineer should:

• Understand what testing activities are happening and when
• Know the history of test results across the CI/CD cycle
• Be familiar with the frameworks and environments that QA teams own

With that knowledge, a DevOps engineer can identify which existing test automation frameworks can be reused for other tasks — such as reproducing production issues or validating specific environments — rather than rebuilding work from scratch.

Importantly, adopting DevOps does not mean automating everything. Manual testing continues to play a vital role even in fast-moving CI/CD environments. Test automation excels at validating the “happy path” repeatedly and reliably, but exploratory testing is necessary for:

• Discovering the alternative paths that real users take
• Testing scenarios that are too complex or too context-dependent to automate effectively
• Covering cases where the return on automation investment does not justify the effort

DevOps engineers who find the right balance between automated and manual testing — and who adjust that balance as the system evolves — put their products in the strongest position for continuous, reliable delivery.

5. Non-Functional Skills: Security, Monitoring and Infrastructure as Code

Beyond the functional pipeline, DevOps engineers need competency in several non-functional areas:

• Monitoring and observability — tracking application performance, error rates and infrastructure health in production, and acting on that data.
• Infrastructure as Code (IaC) — using tools such as Terraform or AWS CloudFormation to define and manage infrastructure programmatically, enabling reproducibility and automation.
• Cloud administration — understanding the platforms (AWS, Azure, GCP or others) on which services run.
• Security (DevSecOps) — integrating security practices directly into the pipeline. Because DevOps ships code frequently in small increments, security checks must be continuous rather than a gate at the end of a release cycle. DevOps engineers should take an active role in this, not treat security as someone else’s concern.

In larger teams, individual engineers may specialise in one or more of these areas. Even so, every DevOps engineer should have enough familiarity across all of them to collaborate effectively with specialists and to recognise when something is wrong.

6. Technical Support and Operational Ownership

A DevOps engineer is not just a pipeline builder — they own and operate the tools and environments the entire team depends on, and sometimes customer-facing capabilities too. That ownership comes with operational responsibilities:

• Real-time monitoring — maintaining visibility into system state at all times
• Alerting — ensuring that meaningful alerts fire when something degrades or fails, and that the right people are notified
• Incident response — troubleshooting and resolving issues quickly, whether through online support, runbooks or direct technical intervention

Keeping production systems available around the clock is a core operational expectation for most organisations running modern software. DevOps engineers are central to meeting that expectation.

Bottom Line

DevOps engineering is not a single discipline — it is a combination of communication, toolchain knowledge, programming, testing awareness, security sensibility and operational discipline. The engineers who thrive in this role are those who can hold the whole delivery system together while helping the people around them move faster and more confidently.

As organisations mature their DevOps practices, the role continues to expand — particularly into security, observability and platform engineering. Starting with the skills outlined here gives you a strong, durable foundation for that growth.

Why Security and DevOps Still Don’t Speak the Same Language

Attending a major DevOps conference as a career security professional is a revealing experience. The culture, priorities, vocabulary, and daily pressures of DevOps engineers can feel alien to someone who has spent years in security — and vice versa. Yet the two disciplines are increasingly interdependent. As organisations push security checks earlier into the software development lifecycle (the “shift left” model), the friction between security teams and pipeline owners is becoming a genuine business risk.

The observations below, drawn from candid conversations with DevOps engineers at a dedicated DevOps conference, offer security professionals a clearer picture of how their developer counterparts think about security — and what practical steps can help close the gap.

1. Admit That Security Has a Perception Problem

Many DevOps engineers at security-conscious organisations are actively looking for ways to ship code to production faster. When security gates slow that process down, security teams get labelled as a bottleneck — not a partner. This perception is not always fair, but it is real.

The encouraging news is that many DevOps engineers genuinely want to solve the security problem. The discouraging news is that they often don’t know where to start. This gap is an opportunity for security leaders to step in with concrete tool recommendations and an honest conversation about what security testing inside a CI/CD (continuous integration and continuous delivery) pipeline actually requires.

Starting from a position of empathy — rather than authority — makes that conversation far more likely to succeed.

2. Teach the ABCs of Security Tooling

DevOps engineers often have surface-level familiarity with security product names but struggle to distinguish between fundamentally different tool categories. For example:

• Static Application Security Testing (SAST) — scans source code for vulnerabilities before the application runs.
• Dynamic Application Security Testing (DAST) — tests a running application from the outside, simulating an attacker.
• Software Composition Analysis (SCA) — identifies known vulnerabilities in open-source libraries and dependencies.
• Container security scanning — inspects container images for vulnerable base layers, misconfigurations, and embedded secrets.
• Infrastructure-as-Code (IaC) scanning — checks Terraform, CloudFormation, and similar files for security misconfigurations before deployment.

Security teams that can map these tool categories to specific stages in a DevOps pipeline — and explain the trade-offs in plain language — become far more useful advisors than those who simply demand compliance.

There is a reciprocal obligation here: security practitioners need to learn DevOps vocabulary. Knowing the difference between a Jenkins pipeline stage and a GitHub Actions workflow, or understanding what “breaking the build” means in practice, signals credibility and earns a seat at the table.

3. Acknowledge the “Get Security Off My Back” Mindset

There is often a genuine cultural tension inside engineering organisations: DevOps teams are measured on release velocity and uptime, while security teams are measured on risk reduction. These incentives can point in opposite directions.

Rather than fighting this dynamic, security teams can work with it. Practical approaches include:

• Negotiating placement of scans — agree on which security checks happen inside the pipeline and which happen in a parallel or post-release workflow, so critical scans don’t add latency to every commit.
• Pre-release code scanning — run SAST and SCA checks before a pull request is merged rather than as a blocking gate on deployment.
• Post-release live testing — schedule DAST or penetration testing against staging or production environments outside of peak release windows.
• Incremental adoption — start with a small number of high-value, low-friction checks. Build trust before expanding scope.

Demonstrating that security did not slow a release cycle — or that it caught a real vulnerability before it reached production — builds the kind of credibility that makes future conversations easier.

4. Never Break the Build Without a Plan B

One of the most pointed pieces of feedback heard from DevOps engineers on security tooling is a simple principle: when in doubt, rip it out. If a security scanner causes a pipeline to fail unpredictably, engineers will remove it — with or without security team approval.

This is not a hypothetical risk. Automated vulnerability scanners can produce false positives, time out under load, or fail to connect to upstream threat-intelligence feeds — all of which can halt a build. Security teams that introduce tools into pipelines without a contingency plan lose trust fast.

Best practices for avoiding this outcome include:

• Run security tools in “audit” or “warn” mode initially — report findings without blocking the pipeline until confidence in the tool’s accuracy is established.
• Define a clear escalation path for failures — if a scanner goes down, the pipeline should continue with an alert sent to the security team, not a hard stop.
• Agree on severity thresholds in advance — only critical or high-severity findings should block a release; medium and low findings should be logged for triage.
• Test the scanner itself in a staging pipeline — before requiring it in production workflows, validate that it performs reliably under real load.

A security tool that engineers trust to behave predictably is one that stays in the pipeline long-term.

Building a Shared Security Culture

The gap between security and DevOps is not primarily a technology problem. It is a culture and communication problem — one that requires security professionals to invest time in understanding how DevOps teams work, what pressures they face, and how security can be positioned as an enabler rather than an obstacle.

The practical steps are straightforward: learn the vocabulary, pick tools that fit the workflow, negotiate placement rather than mandate it, and never introduce a security gate without understanding what happens when it breaks. Done well, DevSecOps is not a compromise — it is a force multiplier for both speed and safety.

Security professionals who make the effort to cross the cultural divide will find a much more receptive audience on the DevOps side than the initial friction suggests.

The Leadership Challenge: Proving Engineering Value

CTOs and other technology leaders face a persistent challenge: how do you help board members and fellow executives understand how well an engineering team is actually performing? The clearest answer is to demonstrate value through working software. The faster a team delivers software that meets real user needs, the more tangible that value becomes. Building a high-performing technology organization—one capable of continuous delivery—is the most reliable path to achieving that speed.

What Continuous Delivery Is (and Why Larger Companies Resist It)

Continuous delivery (CD) is the practice of releasing software updates frequently and in small increments, keeping the codebase in a state where it can be deployed to production at any time. The concept is well established, yet many large, mature companies still bundle multiple changes into infrequent, large-batch releases—often citing risk management as the reason.

The irony is that large-batch, low-frequency releases carry more risk, not less. Consider a common scenario in e-commerce:

• A company freezes all website changes during the peak holiday season to avoid outages.
• In mid-January, a large accumulated update is deployed all at once.
• Part of the site breaks or order data becomes corrupted.
• Because hundreds of changes went out simultaneously, the team cannot isolate which change caused the problem.
• The only safe option is a full rollback—discarding good work along with the bad and delaying other projects.

With continuous delivery, that company would instead release small, incremental changes throughout the year. Each change is narrow in scope, easier to review, and far simpler to roll back if needed. The result is a lower overall risk profile and a faster feedback loop.

Aligning Delivery Cadence with Client Needs

Before optimizing an engineering team’s internal processes, it is worth ensuring the team is working toward goals that genuinely match client requirements. Continuous delivery only creates business value when it is guided by real user feedback.

Establishing a steady feedback loop does two things:

1. It keeps the team nimble. When feedback arrives regularly, engineers can pivot quickly rather than waiting until a quarterly release cycle to act on new information.
2. It prevents change for change’s sake. Iterating based on actual client demands ensures each release carries genuine value rather than arbitrary feature additions.

Client focus alone is not enough, however. Teams must also define expectations and measure progress against them using concrete metrics.

Key Performance Metrics That Continuous Delivery Improves

Several standard DevOps metrics reflect how well a continuous delivery practice is working. The following three are particularly relevant to customer-centric engineering organizations.

Lead Time for Changes

Lead time measures how long it takes from committing code to having that change running in production. Large changes require more stakeholders, more approvals, and more coordination—all of which extend lead time and concentrate risk into single deployment events. Continuous delivery shortens lead time by enabling pre-approved standard changes or lightweight electronic approval workflows, reducing the human capital and calendar time required to ship each increment.

Mean Time to Recovery (MTTR)

MTTR measures how quickly a team can restore service after a failure. With infrequent large releases, diagnosing the root cause of a failure is difficult because too many variables changed simultaneously. Continuous delivery narrows the blast radius of each release, making it far easier to identify a faulty change, revert it specifically, and restore service—often without a full rollback.

Change Failure Rate

Change failure rate tracks the percentage of deployments that result in a degraded service or require an emergency fix. Small, incremental changes are inherently easier to reason about during code review and carry a narrower blast radius if something goes wrong, which naturally drives the failure rate down. A commonly cited target range for high-performing teams is 0–15%, though the right target will vary by organization and system criticality.

Engineering Practices That Support Continuous Delivery

Continuous delivery does not operate in isolation. It depends on a set of engineering disciplines that act as checks and balances throughout the development process:

• Pair programming: Two engineers working together on the same problem surfaces issues earlier and produces more thoughtfully constructed code before a single line is reviewed formally.
• Code reviews: A structured second look on top of pair programming catches issues that even attentive pairs can miss and spreads knowledge across the team.
• Test-driven development (TDD): Writing tests before writing production code keeps development cycles short, supports confident refactoring, and reduces regressions by anchoring changes to specific, verifiable behaviors.

Together, these practices create a quality safety net that makes it safe to release frequently without sacrificing stability.

Leadership Buy-In Is Non-Negotiable

Adopting continuous delivery requires organizational change, not just technical change. Tooling and pipelines are relatively straightforward to implement; changing how an organization thinks about risk, approval processes, and release cadence is harder.

Ironically, large organizations stand to gain the most from continuous delivery—and face the most internal resistance to it. Unnecessary process layers and ingrained risk-aversion policies are the most common barriers. Technology leaders must actively make the case that frequent, small releases reduce risk rather than increase it, and that the slower alternative is what truly puts the business in danger.

High-performing large engineering organizations that have successfully adopted continuous delivery tend to deploy significantly more often than their slower-moving peers—a measurable and meaningful difference in competitive capability.

Recommended Reading

For a rigorous, research-backed treatment of these ideas, the book Accelerate: The Science of Lean Software and DevOps by Nicole Forsgren, Jez Humble, and Gene Kim is the definitive starting point. It presents the empirical evidence behind why continuous delivery and related practices predict both technology and business performance outcomes—essential reading for any leader considering this path.

Summary

Continuous delivery is not simply a deployment technique—it is an organizational capability that improves lead time, reduces recovery time, lowers change failure rates, and aligns engineering output with real customer needs. Building that capability requires the right engineering practices, a culture of feedback, and leadership willing to challenge the assumption that slower, larger releases are safer. Done well, continuous delivery is one of the clearest differentiators between high-performing technology organizations and those that struggle to demonstrate value.

Gone are the days when teams could work on a project for months (or even years) before releasing it to production. Now even two-week cycles are too long due to ever more demanding customer expectations and an always-growing field of competitors.

Organisations today expect to deliver new features to production on a weekly, daily or even hourly basis. This accelerated timeline lets organisations adapt to market shifts and technological changes. It keeps companies on the same pace as their competitors. Most importantly, it enables businesses to continuously deliver value to customers.

There is no question that being better, faster and more reliable is beneficial. But how can businesses achieve that? The short answer is through DevOps.

DevOps saves time by speeding delivery and eliminating lean time

Which phases of the software development life cycle take the most time? The answer, perhaps surprising, is none of them. The most time is wasted not in any one phase, but in between phases. Developers wait for business analysts to give them requirements. Testers wait for developers to finish their code, and developers wait for testers to tell them whether the code works. Both developers and testers wait for system administrators to deploy new releases to different environments. And sysadmins wait for everyone else to tell them why deployments failed. 

The sad truth is that in many companies a significant effort is spent on tasks that do not bring value to the company. Waiting does not bring value, yet many organisations have processes in place with time built in for waiting for another team or colleague to do something. Repeating the same set of manual tests over and over again does not bring value, yet this is a normal practice for many businesses. Handing over deployments to a different department when the team in charge of an application could do it themselves by executing a single command does not bring value, yet there are often separate departments for deployment.

DevOps enables seamless collaboration between teams

Who is in charge of a product? The answer is usually “no one in particular”. Everyone is in charge of a particular aspect or phase of the development life cycle, but no one is in charge of a product from beginning to end. And to make things even more complicated, employees often focus solely on their assigned roles.

Developers don’t always consider how their work affects testing and deployment, since it’s not their responsibility. They may not even know how their applications are tested or deployed. The same can be said for all other departments. Sysadmins likely do not consider how much time others spend opening Jira tickets in order to deploy something. No one team considers the complete life cycle of an application, because responsibility is split among too many silos. There is a lack of empathy. Communication between teams is limited and one team does not understand how their actions affect another.

DevOps addresses the disconnect between teams by building empathy between all those involved in an application’s life cycle. The practice unites distinct teams into a single team that responds to a single product owner. It dismantles silos by creating self-sufficient teams fully in charge of everything related to their application from start to finish. Such teams are in charge of requirements, development, testing, deployment to production, and even monitoring and pager duty. They are in full control of what’s happening with their application.

As a result, there is nothing to be handed over to other teams or silos. There is no need for inefficient handovers in the form of Jira tickets, emails or other administrative hurdles. DevOps is all about cultural changes aimed at creating autonomous and self-sufficient teams in charge of the entire life cycle of one or more applications. Therefore, creation of “DevOps departments” and employment of “DevOps engineers” completely misunderstands what DevOps tries to accomplish.

DevOps is not about creating more silos, nor it is about renaming existing departments as “DevOps departments”. Instead, it’s about people working together to accomplish a common goal: successful release of new features to production.

When everyone works as a single team focused on a single product, communication improves, the need for administrative overhead decreases and ownership is established. Working together and understanding how one department’s actions affect others create empathy. As a result, productivity and quality increase, and costs and time to market decrease. 

Eliminating lean time and automating repetitive processes inevitably increases the time teams have at their disposal. Saved time combined with better cooperation between those involved in application life cycles allows teams to focus entirely on actions that bring value by dedicating precious time and effort to solving problems and innovating.

For real innovation to happen, everyone in the company needs to be involved. Elimination of wasted time and resources, improved collaboration and decentralised innovation are the three key ingredients that allow organisations to focus on what really matters. They allow businesses to move from trying to catch up with competitors to being able to innovatively tackle today and tomorrow’s challenges.

Interested in hearing industry leaders discuss subjects like this and sharing their experiences and use-cases? Attend the Cyber Security & Cloud Expo World Series with upcoming events in Silicon Valley, London and Amsterdam to learn more.

The Pressure on Modern DevOps Teams

For years, IT and DevOps professionals have faced a consistent mandate: be faster, be more proactive, and handle more complexity—without a proportional increase in headcount. The answer has generally been automation. Manual processes that once governed infrastructure provisioning, deployment pipelines, and incident response have steadily given way to scripted, then programmatic, and now increasingly intelligent alternatives.

Cloud computing was an early inflection point. It removed the bottleneck of waiting for physical hardware by making infrastructure available through an API call, freeing engineers to focus on higher-value work. But cloud adoption also introduced new complexity: dynamic, ephemeral environments generating enormous volumes of telemetry data that traditional monitoring tools were never designed to handle.

Why Traditional Monitoring Falls Short

Legacy monitoring tools were designed for relatively static, on-premises infrastructure. They struggle with three specific characteristics of modern cloud-native environments:

• Volume: Microservices, containers, and serverless functions generate orders of magnitude more log and metric data than monolithic applications ever did.
• Velocity: Data arrives continuously and in real time. Batch-oriented analysis pipelines introduce latency that makes them unsuitable for incident detection.
• Variety: Modern stacks produce structured metrics, unstructured logs, distributed traces, and event streams—often simultaneously.

To cope, many monitoring solutions resort to sampling: they ingest a subset of data rather than the full stream. This keeps dashboards manageable but creates blind spots. Critical failure patterns hidden in the discarded data never surface, and root-cause analysis becomes guesswork. The business impact of a missed anomaly—a slow memory leak, an unusual spike in failed authentication attempts—can be significant.

What Is AIOps?

AIOps (Artificial Intelligence for IT Operations) is the application of machine learning, statistical analysis, and automation to IT operations workflows. Rather than replacing DevOps engineers, AIOps augments them by handling tasks that are computationally intensive or too high-volume for humans to perform manually.

At its core, an AIOps platform ingests all available operational data—logs, metrics, traces, events—and applies ML models to identify patterns, correlate signals across disparate sources, and surface actionable insights. Crucially, these models are not static: they retrain continuously as new data types are introduced, maintaining accuracy as the environment evolves.

How AIOps Integrates with DevOps Workflows

Pattern Detection and Anomaly Identification

Once an ML model is trained on your system’s baseline behaviour, it can detect deviations that would be invisible to threshold-based alerting. A gradual increase in database query latency over several hours, for example, may never breach a static alert threshold but can signal an impending bottleneck. AIOps platforms surface these patterns before they become outages.

Automated Trigger-Based Remediation

When a known event pattern is detected, an AIOps system can execute a predefined remediation workflow automatically. A practical example: if CPU utilisation climbs because the number of incoming connections is increasing, the platform can trigger Kubernetes to spin up additional pod replicas, with a load balancer distributing traffic across them—all without human intervention. The DevOps team defines the trigger logic and approves the automation; the AIOps layer executes it consistently at any hour.

Root-Cause Analysis

Correlating an alert in a frontend service with a database slow-query log and a recent deployment event is something a skilled engineer can do—but it takes time. AIOps platforms correlate signals across the entire stack automatically, shortening the mean time to identify (MTTI) and mean time to resolve (MTTR) incidents. Engineers receive a ranked list of probable causes rather than a wall of undifferentiated alerts.

Predictive Failure Detection

Beyond reacting to current anomalies, more mature AIOps implementations attempt to predict failures before they occur. By analysing historical event sequences, the system can identify precursor patterns—sequences of events that have historically preceded a service outage—and alert teams or trigger preventive actions ahead of time.

Business Benefits of Adopting AIOps

• Improved service availability: Proactive detection and automated remediation reduce unplanned downtime, directly improving end-user experience and SLA adherence.
• Elimination of data silos: AIOps platforms ingest data from multiple observability tools, breaking down the fragmentation that occurs when logs, metrics, and traces live in separate systems.
• Engineer productivity: Automating repetitive operational tasks—restarting services, scaling resources, clearing queues—frees engineers to focus on architecture improvements and feature development.
• Cloud cost management: By dynamically monitoring infrastructure behaviour and public cloud utilisation, AIOps can surface over-provisioned resources and help enforce cost controls.
• Better decision support: In-depth log analysis and impact modelling give engineering managers visibility into how infrastructure changes affect business outcomes, supporting more evidence-based decision making.

AIOps and the Evolution of the DevOps Role

A common concern is that AIOps will displace DevOps engineers. The more realistic near-term trajectory is that it changes what those engineers spend their time on. Routine on-call tasks—triaging noisy alerts, manually scaling infrastructure, digging through logs to find the source of an error—are candidates for automation. The engineering judgment required to design resilient systems, define automation policies, interpret model outputs, and respond to genuinely novel failure modes remains firmly human territory.

The broader trend is that the boundaries between development, operations, data engineering, and security are blurring. AIOps is one expression of that convergence, alongside related methodologies like DataOps (which applies similar principles to data pipelines) and DevSecOps (which integrates security tooling into the delivery pipeline).

Where Is AIOps Headed?

AIOps adoption is still maturing. Early implementations focused primarily on log analysis and alert correlation. The next wave of capability centres on more sophisticated areas:

• Automated root-cause analysis that spans complex, multi-service dependency chains.
• Failure prediction based on learned event sequences rather than static thresholds.
• Security operations integration, where anomalous network traffic patterns are used to trigger automated intrusion-detection responses.
• Self-healing infrastructure, where the system not only detects and diagnoses problems but autonomously remediates them within operator-defined guardrails.

The microservice architectures that have become standard in large-scale engineering organisations create exactly the kind of high-volume, high-velocity operational environment where AIOps delivers the most value. As those architectures continue to proliferate—and as the tooling matures—the question is less whether AIOps will become a standard component of DevOps practice, and more how quickly organisations will build the data pipelines and ML expertise needed to realise its potential.

Key Takeaways

• Traditional monitoring tools cannot keep pace with the data volume, velocity, and variety generated by modern cloud-native infrastructure.
• AIOps applies machine learning to operational data to detect patterns, correlate events, and automate responses at scale.
• Common use cases include anomaly detection, automated remediation (e.g., auto-scaling in Kubernetes), root-cause analysis, and predictive failure detection.
• The technology augments DevOps engineers rather than replacing them, shifting their focus from reactive firefighting to proactive system design.
• The field is evolving rapidly; organisations that build the foundational data infrastructure now will be better positioned to adopt more advanced AIOps capabilities as they emerge.

The DataOps Gap in Most Organizations

Despite the growing importance of data-driven software, dedicated data science teams remain rare outside a small number of large enterprises. The practical consequence is straightforward: in most organizations, responsibility for data operations falls on whoever is already managing the technology stack — and that increasingly means DevOps engineers.

If a DevOps team doesn’t take ownership of data operations, the applications that depend on healthy data flows are put at risk. Recommendations engines, predictions features, monitoring pipelines, and security tooling all rely on data moving reliably through the system. Ignoring DataOps is not a neutral choice — it is an active risk.

What Is DataOps and Why Does It Belong in the Delivery Pipeline?

DataOps refers to the collection of processes, workflows, and tooling required to collect, store, manage, and analyze data reliably and at scale. The term draws a deliberate parallel with DevOps: just as DevOps applies agile and automation principles to software delivery, DataOps applies similar thinking to data management.

Historically, DataOps was siloed from software delivery. Application developers, operators, and data specialists worked in separate teams with separate toolchains. That separation was manageable when data volumes were small and data’s role in applications was peripheral.

That is no longer the case. Modern applications are often data-first by design:

• Feature functionality — recommendation engines, personalization, and predictive features all require continuous, high-quality data input.
• Observability — application health monitoring depends on metrics, logs, and traces flowing correctly through data pipelines.
• Security operations — threat detection and incident response rely on fast, accurate analysis of event data.

A broken data pipeline can be just as damaging to a production application as a broken build pipeline. For this reason, DataOps can no longer be treated as a separate concern — it must be integrated into the overall software delivery lifecycle.

Practical Ways to Integrate DataOps into a DevOps Workflow

DevOps engineers don’t need to become data scientists to handle DataOps effectively. The following principles provide a practical starting point.

1. Automate Data Workflows Like You Automate Code Delivery

The automation mindset that underpins effective DevOps applies equally to data operations. Manual data collection, transformation, and validation steps introduce the same risks as manual deployments: inconsistency, human error, and poor scalability.

Tools that automate ingestion, schema validation, data quality checks, and pipeline orchestration reduce the operational burden on generalist engineers. They also make DataOps failures visible earlier — much like CI/CD surfaces broken builds before they reach production.

2. Use AI-Assisted Data Management Where Available

A growing category of data management tooling uses machine learning to reduce manual decision-making in DataOps workflows — for example, automatically detecting anomalies in data pipelines, suggesting data quality rules, or classifying data assets. For teams without deep data expertise, these capabilities lower the barrier to maintaining data health without requiring specialist knowledge for every decision.

Evaluate whether the data tools you already use expose AI-assisted features, and assess purpose-built options against your team’s maturity level and budget constraints.

3. Prioritize Data That Directly Affects Application Behavior

Not all data carries equal weight. Within a software delivery context, it helps to categorize data by its operational impact:

• High impact — data that directly drives application features or user-facing behavior (e.g., training data for a model, session data for personalization).
• Medium impact — operational data used for monitoring, alerting, and post-incident analysis.
• Low impact — data collected speculatively or for future use cases that have not yet materialized.

Applying your team’s limited DataOps capacity to high-impact data first ensures that the most critical pipelines receive the most attention. Not every data set you collect needs to be stored, transformed, and analyzed with equal rigor.

4. Choose Infrastructure-Agnostic DataOps Tooling

Cloud providers and platform vendors often bundle basic data management utilities with their infrastructure. These tools are convenient to start with, but relying on them exclusively can create deep vendor lock-in for your data layer. If you migrate to a different cloud, change your data warehouse, or adopt a new observability platform, proprietary DataOps tooling may need to be replaced entirely — along with all the workflows built on top of it.

Wherever feasible, prefer tools that support open standards and work across multiple infrastructure environments. This keeps your DataOps portable and reduces migration costs over time.

5. Build Baseline DataOps Literacy Across the Team

DevOps engineers don’t need to master statistics or data science, but they do benefit from understanding a core set of data management concepts:

• Data quality dimensions — completeness, accuracy, consistency, timeliness, and validity.
• Pipeline observability — how to identify where data is delayed, dropped, or corrupted within a pipeline.
• Schema management — understanding how schema changes in upstream systems can break downstream consumers.
• Data lineage basics — being able to trace where a given data point originates and how it has been transformed.

Targeted internal training sessions, documentation of your organization’s data systems, and pairing with any available data specialists are all effective ways to build this literacy without formal data science education.

Conclusion: DataOps Is Now Part of the DevOps Remit

Modern software delivery pipelines depend as much on the health of data flows as on the health of code deployments. With dedicated data teams absent from the majority of organizations, DevOps engineers are the practical owners of DataOps by default — whether or not that responsibility is formally recognized.

The good news is that the core DevOps disciplines — automation, observability, iterative improvement, and cross-functional collaboration — translate directly into effective DataOps practice. The gap is not one of capability; it is one of awareness and deliberate skill-building. Closing that gap starts with treating data pipelines with the same operational rigor you apply to your software delivery pipeline.

Venture capital investment in the DevOps space has grown substantially in recent years. Already anchored by large technology companies such as Google, Microsoft and Amazon, the DevOps market has moved well beyond a niche sector for startups and into the mainstream enterprise. The surge of investor attention since mid-2018 signals that many in the VC community view DevOps as a significant, durable opportunity rather than a passing trend.

The Market Opportunity

DevOps has matured into a recognised investment category in its own right, comparable in scope to sectors like healthcare or media technology. According to data from Crunchbase, hundreds of vendors worldwide now offer solutions and services within the DevOps marketplace, and some VC firms have dedicated partners specifically tasked with building DevOps-focused portfolios.

The investment thesis is built on a straightforward premise: a company’s ability to compete increasingly depends on its software capabilities. DevOps is the methodology that determines how effectively an organisation can build, test, release and iterate on software. This makes it relevant not just for software-native companies but for virtually every business that relies on digital systems to operate.

There are also distinct layers of opportunity within the market. Early infrastructure investments in internet platforms and cloud computing have given rise to cloud-native architectures, microservices, containers and serverless computing. But there is an equally important opportunity in helping enterprises modernise legacy systems. Most large organisations cannot simply discard their existing technology stack — mainframe infrastructure, for example, remains critical in many industries. DevOps, applied well, spans both new and legacy technology, enabling organisations to release software on business demand regardless of the underlying stack. As today’s cloud platforms eventually give way to the next wave of disruption, DevOps-mature organisations are better positioned to absorb that change.

What Investors Should Look For

For a VC assessing a potential DevOps investment, the following considerations are particularly important.

1. Look for Companies That Practice What They Preach

Continuous improvement is a core DevOps principle. During due diligence, VCs should ask whether the vendor actually applies DevOps practices to its own engineering and delivery processes. A company selling DevOps tooling that runs on a waterfall release cycle internally is a red flag. Teams that genuinely adopt a DevOps mindset build learning organisations — they measure outcomes, iterate on failures, and ship better products faster. Companies that are not building this internal culture are likely losing ground to competitors that are.

2. Evaluate All Three Pillars: Process, Tools and People

DevOps success rests on three interdependent pillars: process, tooling and people. The market is relatively well served on the first two — there are many vendors addressing operating models and automation toolchains. The underinvested area is people.

Even a technically strong DevOps implementation fails without practitioners who understand how to apply DevOps principles in practice. Skilled DevOps engineers are consistently among the most in-demand technical roles, and training, enablement and skills development platforms represent a meaningful gap in the current vendor landscape. Investment opportunities that address all three pillars — not just tooling — are more likely to generate strong returns, because they help customers achieve lasting organisational change rather than simply purchasing software.

3. Invest in Where the Market Is Heading, Not Just Where It Is

DevOps borrowed heavily from lean manufacturing principles, with a strong emphasis on metrics, throughput and waste reduction. The next evolution of the discipline involves applying AI and machine learning to DevOps workflows — automating anomaly detection, predicting deployment risk and optimising pipeline performance.

The logical extension of DevOps is also expanding horizontally. DevSecOps integrates security practices directly into the development pipeline. Broader concepts like BizDevSecOps aim to align business strategy with the technical value stream. These emerging paradigms create space for next-generation solutions, and VCs who identify category leaders early in these adjacent spaces stand to benefit disproportionately.

4. Offer More Than Capital

The DevOps software delivery lifecycle is broad, spanning development, testing, integration, deployment, monitoring and operations. Each stage has its own subcategory of specialised vendors. A VC that simply identifies the category leader and writes a cheque is taking a straightforward but limited approach.

The most effective VC partners bring more than money. They help portfolio companies build community presence, contribute to ecosystem development and establish best practices that raise the profile of the company within the industry. Strategic introductions, technical advisory support, and help building a high-performance internal culture all accelerate growth in ways that capital alone cannot. In a market as ecosystem-driven as DevOps — where integrations, partnerships and open-source contributions significantly affect a vendor’s credibility — this kind of hands-on involvement is a genuine differentiator.

Why DevOps Is a Durable Investment Theme

A common concern with fast-growing technology sectors is whether the investment thesis is sustainable or whether it reflects a temporary bubble. DevOps is unusual in that it is inherently metrics-driven. Every DevOps transformation targets measurable outcomes — deployment frequency, lead time for changes, mean time to recovery, change failure rate. These metrics make it straightforward to validate whether value is being created. When a portfolio company can demonstrate consistent improvement in these indicators for its customers, the return on investment becomes verifiable rather than speculative.

That measurability is one of the strongest arguments for DevOps as a long-term investment category. It is not a methodology that organisations adopt and then discard — it is an operational capability that compounds over time. Organisations that invest in DevOps become progressively faster, more reliable and more competitive, which in turn creates sustained demand for the tooling, platforms and services that enable it.

For venture capitalists, the DevOps market offers a combination of broad applicability, measurable customer value and a genuine skills and tooling gap that is still being filled. The investors most likely to succeed are those who understand the technical depth of the space, look beyond obvious tooling plays, and bring strategic value to the companies they back.

To be a full stack developer or a DevOps engineer, you need to know CI/CD. It is an absolute requirement. If you’re applying for a new technical position and want to be prepared, here are 10 tough Jenkins interview questions and answers for DevOps engineers that employers often ask.

Jenkins interview questions strategies

A good strategy to use to apply to this set of tough Jenkins interview questions and answers for DevOps professionals is to first read through each question and formulate your own response. Then, read our answers. If you disagree, that is OK; just make sure you are ready to articulate your own ideas on the topic.

Jenkins interview questions for DevOps

Here are the 10 sample Jenkins interview questions for DevOps engineers. Before delving into the set of answers, read through this list, and see how comfortable you are coming up with your own responses to these tough Jenkins interview questions.

1. What are the software prerequisites that must be met before Jenkins is installed?
2. What is the syntax Jenkins uses to schedule items such as build jobs and SVN polling?
3. Name a Jenkins environment variable you have used in a shell script or batch file.
4. Name three security mechanisms Jenkins uses to authenticate users.
5. Describe the standard process to configure and use third-party tools within Jenkins.
6. Name two ways a Jenkins node agent can be configured to communicate back with the Jenkins master.
7. How do you take a backup of your Jenkins build jobs in order to prepare for disaster recovery?
8. Name three steps or stages a typical Jenkins pipeline might include.
9. How can you temporarily turn off Jenkins security if the administrative users have locked themselves out of the admin console?
10. Polling a Git repository for new commits is considered a Jenkins anti-pattern. What is a sound alternative to SVN polling?

Tough Jenkins interview questions answered

Here are the aforementioned answers to the advanced Jenkins interview.

1. What are the software prerequisites that must be met before Jenkins is installed?

Since version 2.54, Jenkins requires an installation of the Java Development Kit (JDK). JAVA_HOME should also be configured prior to installation. Version 1.8 of the JDK is the minimum that Jenkins will support.

Jenkins also requires the services of a Jakarta Enterprise Edition (EE) web profile-compliant servlet engine in order to run, although Jenkins comes with an embedded Jetty runtime that can be used if an existing Tomcat, WildFly or WebSphere application server is not available.

2. What is the syntax Jenkins uses to schedule items such as build jobs and SVN polling?

Jenkins uses the cron syntax to schedule various items within the tool.

The cron syntax is represented by five asterisks, with each one separated by a space. The first asterisk represents minutes, the second represents hours, the third the day of the month, the fourth the month itself and the fifth the day of the week. For example, to schedule a build job to pull from GitHub every Friday at 5:30 p.m., the syntax would be: 30 17 * * 4.

3. Name a Jenkins environment variable you have used in a shell script or batch file.

There are a number of environment variables that are available by default in any Jenkins build job. A few commonly used ones include:

• $JOB_NAME
• $NODE_NAME
• $WORKSPACE
• $BUILD_URL
• $JOB_URL

Note that, as new Jenkins plug-ins are configured, more environment variables become available. For example, when the Jenkins Git plug-in is configured, new Jenkins Git environment variables, such as $GIT_COMMIT and $GIT_URL, become available to be used in scripts.

This isn’t one of the most overly advanced Jenkins interview questions and answers, but it does demonstrate that the interviewee has indeed created a scripted Jenkins build job before.

4. Name three security mechanisms Jenkins uses to authenticate users.

Jenkins can authenticate users in one of three ways:

1. Jenkins can use an internal database to store user data and credentials. (This is the default.)
2. Jenkins can be configured to authenticate against a Lightweight Directory Access Protocol server.
3. Jenkins can be configured to employ the authentication mechanism used by the application server upon which it is deployed.

5. Describe the standard process to configure and use third-party tools within Jenkins?

The process to use a third-party tool, such as Node, SonarQube or Git typically follows a four-step process.

1. The third-party software must be installed.
2. A Jenkins plug-in that supports the third-party tool must be installed through the Jenkins admin console.
3. The third-party tool must be configured in the Tools tab of the Manage Jenkins section of the admin console.
4. Finally, the plug-in can be used from within a Jenkins build job. The plug-in will then facilitate communication between the Jenkins build job and the third-party tool.

This is a tough Jenkins interview question for DevOps professionals because not every third-party tool is configured in exactly the same way. For example, Jenkins can be configured to install Maven itself, rather than requiring a pre-existing installation. Similarly, third-party tools, like Checkstyle or JaCoCo, can be downloaded at build time by Maven. So these four steps are not always adhered to strictly, but at a high level, these are the typical steps required to install and configure a third-party Jenkins tool.

6. Name two ways a Jenkins node agent can be configured to communicate back with the Jenkins master.

The tool provides two mechanisms for starting a Jenkins node agent:

1. Launch a Jenkins node agent from a browser window.
2. Launch a Jenkins node agent from the command line.

When a Jenkins node agent is launched from a browser, a JNLP file is downloaded. When it runs, the JNLP file launches a new process on the client machine that runs Jenkins jobs.

To launch from the command line, the agent.jar file is required on the client. This executable JAR file is run from the command line, along with a reference to the slave agent’s JNLP file that is hosted on the server. Like the JNLP file downloaded through a web browser, running this command launches a process on the client that can communicate with the Jenkins master and run Jenkins build jobs when it has idle clock cycles.

7. How do you take a backup of your Jenkins build jobs in order to prepare for disaster recovery?

Each Jenkins build stores its configuration as XML in a subdirectory of the JENKINS_HOME\jobs folder. By copying this folder to a secondary location, the configuration of all of the build jobs managed by the Jenkins master will be backed up as a result. Checking this folder into a source code management tool like Gitisn’t a bad idea either. Knowledge of ways to perform Jenkins Git integration is always looked upon fondly in a DevOps interview.

By simply copying the contents of this folder to a new Jenkins server instance, all of the build jobs described in this folder will be restored the next time the Jenkins server is started.

It’s also worth noting that a history of all build jobs that have been run is written to this folder as well. To really impress the interviewer asking this advanced Jenkins interview question for DevOps, mention that using a .gitignore file and ignoring job-related files will ensure that any Git repository that stores Jenkins project configuration data will make sure only configuration data is stored. Build job history should be archived elsewhere and not stored in a source code repository.

8. Name three steps or stages a typical Jenkins pipeline might include.

A full-blown Jenkins pipeline will build a project from source code, put it through a variety of unit, integration, performance and user acceptance tests, and then, finally, if every test succeeds, deploy a packaged application to an application server, Nexus repository or Docker container. So, three fundamental stages would be:

1. Build
2. Test
3. Deploy

Of course, the best way to implement a Jenkins pipeline as code is to employ many modular steps. To really impress the interviewer asking one of these tough Jenkins interview questions for DevOps engineers, expand on these three stages, and describe a more complete Jenkins pipeline as code example.

9. How can you temporarily turn off Jenkins security if the administrative users have locked themselves out of the admin console?

The JENKINS_HOME folder contains a file named config.xml. When security is enabled, this file contains an XML element named useSecurity that will be set to true. By changing this setting to false, security will be disabled the next time Jenkins is restarted.

<useSecurity>false</useSecurity>

When asked one of these advanced Jenkins interview questions on DevOps security, be sure to emphasize that disabling security should always be both a last resort and a temporary measure. Once any authentication issues are resolved, be sure to re-enable Jenkins security and reboot the CI server.

10. Polling a Git repository for new commits is considered a Jenkins anti-pattern. What is a sound alternative to SVN polling?

Constantly polling a source code management tool like Git or Subversion to check if a new commit has been issued is a waste of clock cycles and should be avoided.

A better approach is to reverse this process and have the source code tool trigger a Jenkins build when new commits happen. With GitHub or GitLab, it is relatively easy to configure a post-commit hook that runs every time a commit is successful. When provided with the URL of the Jenkins build, the post-commit hook can easily trigger a Jenkins build, eliminating the need to have Jenkins constantly poll the source code repository.

Impress on that Jenkins interview

These are indeed a set of tough Jenkins interview questions and answers for DevOps engineers and aspiring cloud-native practitioners to tackle. But if you are comfortable with the responses provided here and can comfortably put these responses into your own words, you’ll have no problem impressing your future employer with your ability to respond to a variety of advanced Jenkins interview questions that cover a wide and disparate range of DevOps topics.

Why Machine Learning and DevOps Belong Together

Modern DevOps pipelines are data-generating machines. Every commit, build, deployment, container restart, and HTTP transaction produces logs, metrics, and traces. At scale, a busy application cluster can produce gigabytes of log data per day, far beyond what any team can manually inspect. The discipline of DevOps was built on the promise of automation and continuous feedback, yet the analysis layer that turns raw data into actionable insight has historically been the weakest link.

Machine learning (ML) addresses that gap. Rather than relying on engineers to define every threshold and alert rule by hand, ML models can learn normal behavior from historical data and flag deviations automatically. That said, simply enabling an ML feature inside a monitoring vendor’s dashboard is not sufficient. Teams need a conceptual understanding of where ML adds value and where its limitations apply.

The Core Challenge: Data Volume vs. Human Capacity

Traditional DevOps monitoring relies on threshold-based alerting: if CPU usage exceeds 80%, fire a PagerDuty notification. This model is simple to configure but has significant blind spots:

• Threshold fatigue. Poorly tuned thresholds produce alert storms that train engineers to ignore notifications.
• Unknown unknowns. A threshold catches what you already know to watch for. Novel failure modes go undetected until a user reports an outage.
• Context blindness. A CPU spike at 3 AM during a batch job is normal. The same spike during peak traffic is not. Static thresholds cannot distinguish the two.

Machine learning models can be trained on historical time-series data to understand normal operating patterns for a given service, time of day, and traffic load. Deviations from those learned baselines—rather than from fixed thresholds—then trigger alerts. This approach is generally referred to as anomaly detection.

Key Areas Where ML Enhances DevOps Practice

1. Moving Beyond Static Thresholds

Outlier detection based on fixed cutoff values only highlights the most extreme events. An ML-based anomaly detector evaluates a data point in the context of the entire historical distribution of that metric. This means it can surface subtle, sustained deviations—a memory leak growing slowly over hours, for example—that a threshold set at a hard maximum would never catch.

2. Learning from Historical Data

One of the most practical applications of ML in DevOps is retrospective analysis. When an incident occurs, the immediate priority is restoring service. Root-cause investigation often happens later—if at all. ML systems trained on historical metrics and logs can surface correlations between events that occurred minutes or hours before a failure, helping teams understand what sequence of conditions led to the outage. This historical learning directly improves the accuracy of future predictions.

3. Unified Monitoring Across Disparate Tools

Most mature DevOps environments run multiple observability tools simultaneously—infrastructure metrics from one platform, application performance data from another, log aggregation from a third. Each tool presents its own view, and correlating across them is a manual, time-consuming task. ML systems designed for AIOps (AI for IT Operations) can ingest signals from multiple sources and present a correlated, unified view of system health, reducing the cognitive load on on-call engineers.

4. Measuring Orchestration Efficiency

Container orchestration platforms such as Kubernetes continuously make scheduling and scaling decisions. ML can be applied to the telemetry produced by the orchestrator—pod scheduling latency, node utilization imbalance, eviction rates—to identify whether the orchestration layer itself is becoming a bottleneck. Patterns that indicate suboptimal bin-packing or resource contention can be surfaced before they cascade into user-visible degradation.

5. Proactive Fault Pattern Recognition

Many failure modes have recognizable precursor signatures in telemetry data. A disk filling up, a connection pool exhausting, a dependency starting to respond slowly—these patterns can often be detected before the actual failure event. By training a classification model on labeled historical incidents, teams can teach an ML system to recognize those signatures early and trigger automated remediation workflows or targeted alerts before the user impact occurs.

6. Root-Cause Analysis at Depth

Getting a service back online quickly is the primary goal during an incident. But if the root cause is never identified and addressed, the same failure will recur. ML-assisted root-cause analysis tools correlate anomalies across services, trace data, and deployment events to produce a ranked list of probable causes. This does not replace human judgment, but it significantly reduces the time an engineer spends manually correlating logs to identify the source of a problem.

Real Barriers to ML Adoption in DevOps Teams

Despite the clear potential, applying ML in a DevOps context is not without friction. Several practical barriers slow adoption:

• Skill gap. Most DevOps engineers are infrastructure and automation specialists, not data scientists. Understanding which ML algorithm is appropriate for a given problem—and how to evaluate model accuracy—requires expertise that is not yet common on DevOps teams.
• Data quality. ML models are only as good as the data they are trained on. Inconsistent labeling of incidents, gaps in metric collection, and high-cardinality log formats all degrade model performance.
• Black-box opacity. Many ML models do not explain why they flagged a particular event. For on-call engineers who need to act quickly, an unexplained alert is nearly as useless as no alert. Explainability is an active area of development in the ML tooling space.
• Vendor lock-in risk. Several observability vendors have embedded ML capabilities into their platforms. While these lower the barrier to entry, they also create dependency on proprietary implementations that may not be portable or customizable.

Practical Starting Points for DevOps Teams

Teams that want to begin incorporating ML into their DevOps workflows without hiring a dedicated data science team can take a pragmatic, incremental approach:

1. Start with anomaly detection on existing metrics. Most modern observability platforms (such as Datadog, Dynatrace, New Relic, and open-source options like the Prometheus/Grafana ecosystem with ML plugins) offer built-in anomaly detection. Enable it on your highest-value service-level indicators first and evaluate alert quality before expanding coverage.
2. Label your incidents consistently. Good historical data is the foundation of useful ML. Adopt a structured incident taxonomy and ensure postmortems are machine-readable. This data will train better models over time.
3. Treat ML alerts as signals, not verdicts. An ML-generated alert should direct human attention, not trigger automated rollbacks without human review, at least until the model has a demonstrated track record on your specific infrastructure.
4. Invest in explainability. When evaluating ML tools, preference those that provide a rationale alongside their output. A tool that says “anomaly detected on payment-service latency, correlated with a deployment 12 minutes ago” is far more actionable than one that only says “anomaly detected.”
5. Build feedback loops. Alert the ML system when it was wrong. False positive and false negative feedback refines future model behavior. Without this loop, model accuracy tends to drift as the system evolves.

Conclusion

Machine learning does not replace DevOps engineering judgment—it augments it. The combination of agile software delivery practices and ML-powered analysis creates a feedback loop that is faster and more sensitive than either discipline achieves alone. The teams that will benefit most are those that invest in data quality, start small with proven anomaly detection use cases, and treat ML outputs as one signal among many rather than as an infallible oracle.

As ML tooling matures and the skill gap between DevOps and data science narrows, the operational intelligence available to engineering teams will only increase. Building familiarity with these tools now positions teams to leverage more sophisticated capabilities—predictive autoscaling, automated root-cause isolation, and self-healing infrastructure—as they become production-ready.

If your team has already adopted continuous integration, automated testing, or infrastructure as code, you are ready to ask the next question: what does a more mature DevOps practice look like? The steps below offer a structured path forward.

What Is DevOps?

DevOps is a set of practices, cultural philosophies, and tooling patterns that break down the traditional silos between software development, QA, and IT operations. By bringing these disciplines into a single, cross-functional team that owns the entire product lifecycle — from commit to production — DevOps enables faster delivery, fewer human errors, better version control, and more efficient use of engineering resources.

Crucially, DevOps is not just a toolchain. It is a combination of people, process, and technology. Introducing a CI/CD pipeline without changing how teams communicate and share ownership will produce only marginal improvements. Genuine DevOps transformation requires cultural change alongside technical investment.

6 Steps to Advance Your DevOps Practice

1. Flatten Team Hierarchies and Establish End-to-End Ownership

One of the most significant obstacles to DevOps maturity is the persistence of vertical, siloed team structures. When developers only see their slice of the codebase and operators only see the infrastructure, no one has the full picture needed to optimize delivery end to end.

Moving toward a flatter organizational model — where cross-functional product teams own a service from development through deployment and monitoring — aligns directly with core DevOps principles. This means giving engineers visibility into production behavior, giving operations teams input into architecture decisions, and holding everyone accountable for the health of the whole system, not just their individual component.

2. Adopt Container-Based Architecture and Microservices

Containers (such as those managed by Docker and orchestrated by Kubernetes) and microservices architectures are natural complements to DevOps. They allow large, monolithic applications to be decomposed into independently deployable services, each owned by a small team that can build, test, and release on its own cadence.

Containers provide consistency between development, staging, and production environments, which eliminates a significant class of environment-related bugs. Microservices make it easier to scale individual components without redeploying the entire application. Together, they increase team autonomy and reduce the coordination overhead that slows delivery in large organizations.

3. Foster Healthy Competition Through Transparent Metrics

Visibility across teams is a powerful accelerant for continuous improvement. When teams can see how they are performing relative to each other — on dimensions like deployment frequency, mean time to recovery (MTTR), change failure rate, and lead time for changes — healthy competition naturally emerges.

This does not mean ranking teams punitively. Rather, it means publishing shared dashboards that make performance data transparent and actionable. Teams that see a peer group deploying twice as frequently will look inward for bottlenecks. This peer visibility is far more motivating than top-down mandates, and it drives organic knowledge-sharing as high-performing teams become a reference point for others.

The four DORA metrics — deployment frequency, lead time for changes, change failure rate, and MTTR — are a widely used starting point for this kind of benchmarking.

4. Expand Automation Across the Entire Pipeline

Speed and scale are core promises of DevOps, and neither is achievable without extensive automation. If your team has automated unit tests and basic CI builds, the next layer of automation worth pursuing typically includes:

• Integration and end-to-end testing — automated test suites that validate behavior across service boundaries.
• Infrastructure provisioning — using tools like Terraform or Ansible to codify and automate environment creation.
• Security scanning — shifting security checks left by embedding SAST, DAST, and dependency vulnerability scanning into the CI pipeline.
• Release and deployment automation — progressive delivery strategies such as blue/green deployments, canary releases, and feature flags.
• Compliance and configuration drift detection — automated checks that ensure infrastructure remains in its desired state.

Beyond the efficiency gains, automation removes repetitive, error-prone manual work from engineers’ plates — work that drains motivation and diverts focus from higher-value problem-solving.

5. Build Fast Feedback Loops at Every Stage

The faster a team learns about a problem, the cheaper it is to fix. This principle applies both to technical feedback (test failures, production alerts, build errors) and to team-level feedback (code reviews, sprint retrospectives, incident post-mortems).

Practically, this means:

• Keeping CI build times short so developers get test results in minutes, not hours.
• Instrumenting production services with meaningful observability (logs, metrics, traces) so issues surface before users report them.
• Running regular retrospectives where teams openly discuss what slowed them down or caused incidents.
• Treating post-mortems as blameless learning exercises rather than exercises in assigning fault.

Fast feedback is not only a technical concern. Managers and leads should also close the loop quickly with individual engineers — acknowledging good work, flagging problems early, and providing context that helps engineers make better decisions independently.

6. Secure Leadership Commitment and Organizational Accountability

DevOps transformations stall most often not because of tooling gaps, but because of insufficient leadership buy-in. If senior leadership treats DevOps as a purely technical initiative owned by engineering, the cultural and organizational changes necessary for it to succeed will not happen.

Effective DevOps at scale requires leaders to:

• Actively fund and protect the time teams need to reduce technical debt and improve internal tooling.
• Remove organizational blockers — such as approval processes or procurement cycles — that create artificial delays in delivery.
• Model the collaborative, blameless culture they want to see in their teams.
• Hold themselves accountable for providing clear priorities, so teams are not constantly pulled in competing directions.

When accountability flows from leadership downward and shared ownership flows across teams, DevOps practices take root and compound over time.

DevOps Is a Journey, Not a Destination

The organizations that get the most from DevOps treat it as a continuous improvement system rather than a one-time implementation project. Each of the six steps above builds on the others: flatter teams communicate faster, automated pipelines make feedback loops tighter, and transparent metrics sustain the accountability that keeps improvement going.

Whatever your current level of DevOps maturity, the next step is always the same — identify your biggest constraint, address it systematically, measure the result, and repeat.

DevOps has become an important and inseparable part of every business today. The rise of DevOps has meshed the development and operations teams together, largely contributing to the faster development and deployment of software.

Despite the most obvious problems that DevOps solves, there are a few common mistakes that DevOps teams and their organizations continuously commit while working together to deliver the companies’ products.

We have chosen to highlight the 5 most common mistakes your DevOps team is most likely to make.

Quickness Over Quality

In an attempt to meet the urgent need to develop software in accordance with the business needs, some organizations ignore the inexperience or lack of complete knowledge of DevOps and employ IT personnel who struggle to deliver effective DevOps outcomes. This can result in lower quality work making it out the door, as well as frustration and difficulty keeping up with competitors.

Another issue at hand when you put quality second is the concern over upcoming deadlines. DevOps professionals are often in a mad pursuit of getting the job done to meet their tight schedule. Unfortunately, this can sometimes lead to insufficient testing, which can result in security issues and bugs in the later stages of production.

Staying in the Experimental Stage

Organizations which are new to DevOps need to remember that you have to crawl before you can run.  Most DevOps experts advise starting small when adopting DevOps, and organizations appear to be following that advice.

However, starting small might be the right call when just starting out but that doesn’t mean you should stay small moving forward. When companies aren’t deploying at a high frequency this can cause more issues from not having the right processes in place for optimized activities. Some companies might experience higher costs, slower growth, longer development times and lower customer satisfaction than they would be if they had utilized DevOps more frequently.

Moving with the Old and the New

Some organizations tend to hold on to old practices and personnel while incorporating DevOps. This is known as a ‘Hybrid DevOps’ model. In this model, some parts of the processes are streamlined whereas the other parts are allowed to continue at their earlier pace.

While the decision to adopt a hybrid model is often made considering the organization’s existing capital and resources, this move may backfire as the newer way of  working cannot always coexist with age-old practices, leading to disruptions and frustrations.

No Monitoring Required

It would not be wise of organizations to believe that the role of DevOps engineers or the operations personnel ends with the release of the application or software. Fact is that they need to keep their head in the game or risk losing ground.

DevOps processes and tools need constant monitoring and overseeing. Your DevOps team has to constantly ensure the availability of budgets, additional goals, resources, and the continuous functioning of the software. A well-thought-through DevOps implementation plan never quits learning at any stage and neither does it stop improving moving forward.

What About Sec in DevOps

In the race to keep up, security is often the first casualty of the move to DevOps. However, thanks to automated solutions like Software Composition Analysis which organizations can use to set and enforce policies that can block open source components with known vulnerabilities from entering the product, security does not have to be left by the wayside in the name of speed and efficiency.

DevOps thrives on the concept of “shifting left” as much as possible in order to achieve a better outcome. By adding security to the mix with automated solutions, DevOps teams can focus on getting their product out the door, knowing that they are coding securely and minimizing the amount of time that would otherwise be spent remediating issues before release.

DevOps is a smart practice that is a result of the two of the core teams of every business. If applied correctly,it can give a company the edge against the competition, coming out with faster releases which are secure and reliable. Learn from the mistakes of others and do DevOps right, letting your team focus on building something incredible.

The Tension Between DevOps and Organisational Structure

DevOps is fundamentally about breaking down barriers between development, operations, QA, and every other team involved in software delivery. That principle has driven some of the most significant improvements in software engineering over the past decade. But there is a point at which dismantling barriers goes too far — and the resulting lack of structure becomes its own bottleneck.

A healthy DevOps organisation still needs clearly defined roles, accountability, and a chain of command. DevOps does not advocate for the total erasure of structure; it advocates for removing the artificial silos that prevent collaboration. Understanding the difference between those two things is the foundation of a sustainable DevOps team structure.

What Good Cross-Functional Collaboration Actually Looks Like

In a well-structured DevOps environment, engineers regularly step outside their primary roles — but not so far that their core responsibilities are neglected. Typical examples include:

• IT operations engineers joining conversations about code architecture to surface deployment and infrastructure constraints early in the development cycle.
• Developers learning enough about testing tooling — such as Selenium — to interpret automated test results without needing to hand off every question to QA.
• QA engineers participating in release planning so that quality gates are built into the pipeline rather than bolted on at the end.

This kind of deliberate, bounded collaboration is precisely what makes DevOps effective. The problem arises when the boundaries disappear entirely.

Five Warning Signs Your DevOps Structure Has Gone Too Far

How do you know when your organisation has crossed the line from healthy collaboration into structural dysfunction? Watch for these five signals.

1. You Only Hire “DevOps Engineers”

Generalist DevOps engineers who can float between teams and facilitate collaboration are genuinely valuable. But if your entire engineering roster consists of people with vaguely defined, overlapping responsibilities, you will struggle to reach real depth in any discipline. You still need dedicated coders, dedicated operations engineers, dedicated security practitioners, and so on. Breadth-first hiring is not a substitute for genuine specialist expertise.

2. No One Is Clearly in Charge

Removing silos and flattening hierarchies are two very different things. DevOps does not require an entirely flat organisation. Teams still need leads, owners, and decision-makers. Without a clear chain of command, routine decisions become unnecessarily slow and high-pressure situations — such as an incident during a production outage — can descend into confusion about who has authority to act.

3. Software Delivery Is Getting Slower, Not Faster

One of the central promises of DevOps is faster, more reliable software delivery. If you have adopted the standard tooling and practices — CI/CD pipelines, automated testing, infrastructure as code — but your lead times and deployment frequency are not improving (or are getting worse), the root cause may be structural rather than technical. When roles are too loosely defined, engineers spend significant time figuring out who is responsible for what instead of actually delivering value.

4. Everyone Gets Blamed When Something Goes Wrong

Shared ownership is a DevOps virtue. Diffuse, unassigned ownership is a liability. If no individual or sub-team has clear responsibility for a particular system or process, then when that system fails, the entire team becomes the target of blame — which is demoralising and makes post-incident reviews ineffective. Accountability and collaboration are not mutually exclusive. You need both.

5. You Consistently Excel in One Area but Struggle in Others

DevOps aims to lift capability evenly across the full software delivery lifecycle — development, testing, deployment, monitoring, and operations. If your organisation is noticeably strong in some areas but chronically weak in others, it is a signal that your generalist DevOps headcount does not actually cover all the disciplines you need. The fix is targeted hiring: bring in people with the specific skills required to fill genuine gaps, rather than adding more generalists.

Striking the Right Balance

The goal of DevOps is agility, not anarchy. A well-functioning DevOps organisation is characterised by:

• Clear role definitions that allow for collaboration across boundaries without eliminating personal accountability.
• Defined ownership for systems, services, and processes — ideally expressed through practices like on-call rotations and service ownership models.
• A visible chain of command for decision-making, particularly during incidents and major releases.
• Targeted specialist hiring to fill skill gaps that generalist DevOps engineers cannot cover.
• Metrics-driven improvement — if your delivery performance is not improving, treat it as a structural problem to investigate, not just a tooling problem to automate around.

DevOps at its best is a deliberate design choice, not an absence of design. Removing the wrong structures — rigid hand-off processes, approval bottlenecks, communication silos — frees your teams to move faster. Removing the right structures — ownership, accountability, specialisation — creates an organisation that cannot reliably deliver anything.

Revisit your team structure regularly. As your organisation scales, the balance that worked at 10 engineers will not automatically work at 100. Treat your organisational design with the same discipline you bring to your software architecture.

Why Networking Needs DevOps

DevOps culture has transformed software development and operations — but networking has traditionally lagged behind. Manual switch configuration, siloed network teams, and change-averse processes are still common in many organizations. The question Armstrong addresses in his book is whether incorporating networking into a DevOps culture can genuinely boost organizational agility and growth. His answer is a clear yes — and the tooling is rapidly catching up to make it practical.

The Future of DevOps in Networking

Intent-Based Networking

According to Armstrong, the most significant shift on the horizon is intent-based networking (IBN). Rather than configuring each network device individually, intent-based systems allow network architects to define a high-level blueprint of what they want the network to do — and then automatically push that intent to all target devices.

Armstrong cites Apstra as an early example of this model. With an intent-based approach, a network architect can express a desired topology — for instance, a leaf-spine data center architecture — and the system handles the per-device configuration automatically. If the design changes later, the network converges to the new intent without engineers touching each switch manually.

This model has clear parallels with infrastructure-as-code in the software world: you declare desired state, and the platform handles reconciliation. For private cloud and data center environments, IBN promises to abstract away the day-to-day management burden of individual network devices.

Container Networking

Container networking has become a first-class concern as Kubernetes adoption has grown. Armstrong highlights open source projects such as Calico, which provides container networking for both private and public cloud environments alongside Kubernetes. Calico favors network-native principles — using standard IP routing rather than complex overlay networks — making it more approachable for network engineers familiar with traditional routing and switching concepts.

The broader trend is a move away from opaque overlay abstractions toward container network architectures that network teams can reason about using existing skills.

Application-Centric Load Balancing

On the load balancing side, Armstrong points to application-centric software load balancers — such as Avi Networks (now VMware NSX Advanced Load Balancer) — that bring built-in analytics and the ability to scale based on real-time throughput. These platforms expose developer-friendly APIs, making them well-suited for integration into automated deployment pipelines.

The unifying theme across all three areas — intent-based networking, container networking, and modern load balancers — is that networks are being built for automation from the ground up, with APIs that DevOps toolchains can consume directly.

Key Tools and Languages for NetDevOps Engineers

Bridging the gap between traditional networking and DevOps requires network engineers to develop software skills — and software engineers to understand networking fundamentals. Armstrong’s recommended skill set covers four broad areas:

1. Programming and Templating

• Python — The dominant scripting language in network automation. Used for everything from parsing device output to calling network APIs.
• Jinja2 templates — Widely used to render device configurations dynamically from structured data.
• YAML — The standard data format for defining variables, inventories, and playbooks in most network automation frameworks.

2. Configuration Management

• Ansible — The most widely adopted configuration management tool for network automation. Its agentless architecture suits network devices well, and it has extensive modules for vendors such as Cisco, Juniper, and Arista.

3. Version Control

• Git — Treating network configurations as code means storing them in version control. Git is the industry standard, enabling change tracking, peer review, and rollback for network changes — just as it does for application code.

4. CI/CD Pipelines

• Jenkins — A mature, self-hosted CI/CD server with broad plugin support.
• Travis CI — A cloud-based CI service, commonly used for open source projects.
• Concourse CI — A pipeline-centric CI/CD tool favored in environments that want a declarative, container-native approach to build and deployment workflows.

Together, these tools allow network changes to follow the same review, test, and deploy lifecycle as application code — reducing the risk of manual configuration errors and enabling faster, more auditable change management.

Takeaways for DevOps and Network Engineers

• Networks are becoming programmable. Vendor APIs and intent-based platforms mean that manual CLI configuration is no longer the only — or best — option for managing network infrastructure at scale.
• The skills gap is real, but closeable. Network engineers who invest in Python, Ansible, and Git will be significantly better positioned as automation becomes standard practice.
• DevOps principles translate directly. Infrastructure-as-code, version control, CI/CD, and peer review are as applicable to network configuration as they are to application deployment.
• Container networking is not optional. As Kubernetes becomes the default runtime for many organizations, understanding how container networking works — and how to operate it — is increasingly a core networking skill.

For engineers looking to go deeper, Armstrong’s book DevOps for Networking (Packt Publishing) provides a structured introduction to applying DevOps workflows specifically to network engineering challenges.

Ignoring the human dimension of DevOps means leaving significant efficiency gains on the table. The following five strategies address that gap directly.

Why People Management Is Hard in DevOps

Software processes are deterministic: given the same input, you get the same output. People are not. Engineers mishear instructions, interpret requirements differently, and respond to incentives in unexpected ways. These are features of human creativity, not bugs — but they do mean that managing a DevOps team requires a different playbook than managing a CI/CD pipeline.

The good news is that many of the same principles that make software delivery efficient — automation, visibility, modularity, and clear metrics — can be applied to team management as well.

Five Strategies for Effective DevOps Team Management

1. Adopt ChatOps for Visibility and Collaboration

Many teams already use messaging platforms such as Slack or Microsoft Teams for day-to-day communication. ChatOps takes this further by integrating automated alerts, bot-driven commands, and workflow triggers directly into those chat channels. Instead of checking separate dashboards, engineers see deployment events, monitoring alerts, and incident notifications in the same place where they collaborate.

The team-management benefit is often overlooked: ChatOps creates a transparent, searchable audit trail of who did what and when. That visibility makes it easier to coordinate engineers across shifts and time zones without lengthy status meetings or manual handoff documents.

2. Use Automated Scheduling and On-Call Tools

Maintaining 24/7 availability requires engineers to be on call. Manually building and updating on-call rosters is time-consuming and error-prone — gaps appear, fairness suffers, and burnout follows. Dedicated on-call management tools (several are built into DevOps monitoring platforms) automate rotation schedules, escalation policies, and coverage tracking.

Automating scheduling also produces data: you can see which engineers are absorbing the most incidents, identify recurring problem areas, and adjust workloads proactively before burnout becomes a retention problem.

3. Keep Tasks Small and Modular

Assigning a large group of engineers to a monolithic task makes it difficult to track individual contributions, spot bottlenecks, or ensure that nothing falls through the cracks. The same microservices principle that improves software architecture applies to team structure: break work into discrete, well-defined units and assign clear ownership.

• Define specific roles with explicit responsibilities rather than a general pool of “do everything” engineers.
• Scope individual work items so that progress is measurable within a sprint or a single day.
• Use your issue tracker to make every task visible to the whole team, not just the assignee.

Modular task design reduces coordination overhead, makes blockers visible earlier, and gives each engineer a clear sense of ownership and accomplishment.

4. Minimize and Automate Handoffs

A handoff occurs when responsibility for a task transfers from one person or team to another — for example, from development to QA, or from QA to operations. Every handoff is a potential point of failure: context gets lost, communication lags, and errors creep in.

There are two ways to attack this problem:

1. Reduce the number of handoffs by building cross-functional teams that own a feature or service end to end, from code to production.
2. Automate the handoffs that remain so that information — test results, deployment status, approval gates — flows automatically into the next stage without requiring a manual email or meeting.

Pipeline automation tools are the primary lever here, but team structure matters too. The fewer organizational boundaries a piece of work has to cross, the faster and more reliably it moves.

5. Assess Performance Based on Results, Not Hours

In DevOps, the metrics that matter are outcomes: deployment frequency, mean time to recovery, change failure rate, and system uptime. These are results-oriented measures. Tracking hours worked instead of outcomes achieved sends the wrong signal to your team.

When engineers are evaluated on hours, they are implicitly penalized for working efficiently. An engineer who closes a critical incident in 30 minutes looks less productive on a timesheet than one who takes four hours. Results-based assessment avoids this distortion, keeps engineers motivated, and frees managers from micromanaging calendars so they can focus on outcomes that actually matter to the business.

Practical implementation: agree on measurable goals per sprint or quarter, make them visible to the whole team, and tie retrospectives to those goals rather than to activity logs.

Putting It All Together

A fully optimized DevOps practice requires a holistic approach. Streamlining your CI/CD pipeline while leaving team communication fragmented, scheduling manual, tasks monolithic, handoffs manual, and performance metrics time-based is a half-finished job. The five strategies above apply the same principles — automation, visibility, modularity, and outcome focus — to the human side of DevOps. Together, they help your team move as efficiently as the software it ships.

Why Compensation Alone Won’t Build a Thriving DevOps Team

How do you keep software engineers happy and genuinely productive? The answer goes well beyond salary bands and office perks. Engineers who have adopted the DevOps mindset are motivated by more than a paycheck — they care deeply about how they work, what tools they use, and whether the culture around them enables continuous improvement.

Competitive compensation, a comfortable working environment, and performance bonuses are all necessary to attract DevOps talent. But on their own, these factors are insufficient. Organizations that rely on financial incentives alone frequently find that their best engineers remain disengaged, leave for competitors, or quietly underperform within processes they find frustrating.

The real differentiator is culture. To get the most from DevOps professionals — and to keep them — you need to build an environment that aligns with how they think and what they value.

Five Cultural Characteristics That Motivate DevOps Engineers

The following characteristics reflect what DevOps professionals consistently look for in their working environment. Organizations that embed these qualities into their culture are far better positioned to attract, retain, and get the best work out of DevOps talent.

1. Flexible, Overlapping Roles

One of the core premises of DevOps is the breaking down of silos between development, IT operations, security, and other disciplines. Engineers who embrace DevOps expect their day-to-day responsibilities to reflect this philosophy. Rigid job titles that strictly define what an engineer can and cannot do are antithetical to the DevOps model.

This does not mean eliminating roles entirely — structure still has value. But it does mean allowing engineers to step outside their official remit when it makes sense, and actively considering hybrid roles that span traditional boundaries (such as platform engineering or site reliability engineering).

2. Autonomy to Choose Their Own Tools

DevOps practitioners tend to have strong, well-informed opinions about tooling. Mandating a specific tool set without giving engineers any input is a reliable way to generate resentment and reduce effectiveness. Teams that are forced to use tools they find inferior or poorly suited to the task are less efficient and less engaged.

Within the constraints of organisational security, compliance, and interoperability requirements, empower teams to select the tools that best fit their workflow. If a team prefers a different CI tool, a different container orchestration approach, or a different monitoring stack than the rest of the organisation, evaluate the request on its merits rather than dismissing it reflexively.

3. A Safe Environment for Dissent

Continuous improvement — a foundational DevOps principle — is impossible without honest feedback. DevOps engineers want to be able to raise concerns about processes, tooling, and ways of working without fear of political fallout. If team members feel that speaking up will be ignored or penalised, problems accumulate and morale erodes.

Building psychological safety into your engineering culture is not a soft, optional extra. It is a prerequisite for the blameless post-mortems, retrospectives, and iterative process improvements that make DevOps work in practice.

4. Freedom from Unnecessary Redundancy

DevOps engineers are strongly drawn to the principle of building once and deploying anywhere. Repetitive manual tasks that could be standardised or eliminated are a persistent source of frustration. Wherever possible, streamline workflows to remove duplication — in pipeline steps, configuration management, environment provisioning, and deployment procedures.

It is worth noting that redundancy is not always a problem: deliberate redundancy in testing strategies, failover systems, and data backups is a feature, not a bug. The target of simplification should be unnecessary duplication of effort, not resilience architecture.

5. Automation of Tedious, Repeatable Work

DevOps engineers are natural automators. Tasks that are manual, repetitive, and clearly automatable — server provisioning, incident alert routing, backup jobs, schedule management, environment teardown — are deeply unsatisfying when left as toil. Beyond the morale impact, toil is also a practical problem: it consumes engineering time that could be spent on higher-value work.

Encourage and fund automation efforts. Give engineers the time and mandate to reduce toil across the delivery pipeline and operations runbook. This pays dividends both in engineer satisfaction and in operational reliability.

Putting It All Together

Compensation sets the floor. Culture determines the ceiling. Organisations that pair fair pay with flexible roles, tool autonomy, psychological safety, lean workflows, and a commitment to automation create environments where DevOps engineers can do their best work — and want to stay.

The investment required to build this kind of culture is largely organisational rather than financial. It demands deliberate leadership decisions, a willingness to cede some control to engineering teams, and a genuine commitment to continuous improvement at every level of the organisation. The return on that investment — in productivity, retention, and delivery quality — is substantial.

Building DevOps characteristics into your culture is not a one-time initiative. It is an ongoing practice that needs to be modelled from the top and reinforced through everyday decisions about how work gets done.

Because of this culture-first nature, technical ability is necessary but not sufficient. Engineers who prefer extended periods of solo, heads-down work can find the constant feedback loops frustrating. The practitioners who thrive in DevOps environments tend to share a recognizable set of soft skills that complement their technical knowledge. Below are seven of the most important ones.

1. Self-Directed Learning

DevOps tooling and practices evolve faster than almost any other area of IT. CI/CD pipelines, container orchestration, infrastructure-as-code, observability platforms—all of these disciplines have matured dramatically in just a few years, and the pace shows no sign of slowing. If you rely solely on formal training to keep up, you will fall behind.

Anant Agarwal, CEO of edX, puts it this way: “It’s hard to learn something that seems to evolve as quickly as the lessons are taught. Self-learners are the perfect candidates for embracing and pursuing DevOps adoption, as it requires a roll-up-your-sleeves, trial-and-error, do-it-yourself, continuous learning approach.”

Lisa Phillips, VP of Site Reliability Engineering at Fastly, adds an important dimension: “Being a DevOps professional means knowing 1,000 things pretty well, not being a specialist in just one. To be successful, you have to be open and willing to learn every step of the way, and re-learn things that have proven to be wrong—or become wrong over time.”

In practice, this means building habits around reading release notes, following post-mortems published by other engineering teams, experimenting in lab environments, and treating every incident as a learning opportunity.

2. Collaboration

The foundational premise of DevOps is that development and operations are not adversaries but partners working toward a shared goal: reliable, rapid software delivery. That premise only holds if the people involved genuinely embrace a team-first mindset.

Agarwal notes: “DevOps combines both development and operations, and therefore it’s imperative that all team members are able to adopt a team-first mindset.”

Omri Gazitt, Chief Product Officer at Puppet, takes it further: “Any problem is everyone’s problem, and a great engineer always makes those around them better.”

Collaboration in DevOps is not just about attending stand-ups. It means proactively sharing context, reviewing each other’s infrastructure code, pairing on incidents, and being genuinely invested in the outcomes of teammates—not just your own tickets.

3. Communication

DevOps engineers regularly interact with a wide range of stakeholders—developers, product managers, security teams, customer-facing support staff, and sometimes executives. Each audience has a different level of technical fluency, and the ability to tailor your message accordingly is essential.

Ben Porterfield, cofounder and VP of Engineering at Looker, is direct about the cultural shift this represents: “Long gone are the days of the grumpy cave-dwelling Ops persona. DevOps folks need to be able to work with the entire engineering department, with product, and often with a host of other departments that use internal tools. DevOps engineers regularly communicate with people of all levels of technical background and need to be able to adjust their communication level to make sure there is understanding.”

Agarwal connects communication directly to collaboration: “Setbacks and successes, as well as what the team learns each step of the way, must be transparently and immediately communicated across the entire organization. Also, the big-picture end-goal of a DevOps adoption needs to be clearly communicated across the organization—from even before day one.”

Written communication matters just as much as verbal. Runbooks, incident reports, architecture decision records (ADRs), and pull request descriptions are all forms of communication that outlast any single meeting.

4. Resilience

Production systems break. Deployments fail. Architectural decisions that seemed sound last year turn out to be bottlenecks today. DevOps practitioners operate in an environment of constant change, and with change comes failure. The ability to absorb setbacks, adjust course quickly, and keep moving is non-negotiable.

Agarwal frames this as an inherent characteristic of the discipline: “Working in such a fast-moving area of IT demands a lot of work and trial-and-error. DevOps demands new ways of critical thinking and skillsets that are not ingrained in the standard day-to-day responsibilities of business professionals.”

Resilience is also organizational: teams that respond to failures with blame rather than curiosity create environments where engineers hide problems instead of surfacing them early. A resilient DevOps practitioner models the blameless post-mortem culture that keeps systems healthy over time.

5. Big-Picture, Systems Thinking

DevOps practitioners are generalists by nature. But breadth of knowledge only has value if you can see how the pieces connect—how a database configuration change ripples into application latency, how a Kubernetes node autoscaling policy affects downstream services, or how a deployment pipeline bottleneck slows the entire engineering organization.

Gazitt describes this as caring about outcomes rather than just one’s own contribution: “DevOps requires a generalist mindset. Specialized skills are important, but it’s even more important to be able to understand and assist in removing bottlenecks in the overall system. By caring about the outcome as opposed to merely their part in the overall ‘supply chain,’ they will have the right instincts around how to best optimize for end-to-end success.”

Greg Warden, VP of Engineering at DigitalOcean, uses a specific term for this capability: “I think it’s important for people working in DevOps to be good ‘systems thinkers.’ By that I mean you need to be able to think in terms of dependencies and potential failure modes. You have to understand who might be impacted by any changes to the system or application you are working on, and how your system might be impacted by changes or failures in another person’s system or infrastructure. It is also important to understand the resources your systems use, and how those change as your system scales over time.”

6. Prioritization

A DevOps engineer’s backlog is never empty. On any given day you might face a production alert, a security patch that needs rolling out, a developer asking for a new environment, a performance regression to investigate, and a platform migration that’s been blocked for three sprints. The ability to triage effectively—distinguishing between urgent and important, and between noise and signal—directly determines how much value you deliver.

Eli Flesher, Engineering Manager at Lyft, articulates the central tension: “The ability to prioritize is extremely important in DevOps. There is an ongoing tension between developing for the future and operating what’s right in front of you. Some of the best DevOps people I’ve seen have the ability to stay focused on the broader goal of operations and the things they need to build for the future, while also prioritizing the timely, more short-term pressing tasks at hand.”

Practical prioritization frameworks—such as impact-versus-effort matrices, SLO-based alerting policies, or simply a clearly maintained team backlog—help make this skill concrete and visible rather than leaving it to intuition alone.

7. Empathy

Close collaboration inevitably means working through mistakes, miscommunications, and missed deadlines—together. The DevOps professionals who build the most effective teams are those who can put themselves in a colleague’s position before reacting, especially during stressful incidents.

Phillips captures this well: “Things will break—failure is part of the game and blaming others never helps. A strong DevOps professional will put him/herself in the shoes of their team members and act accordingly.”

Empathy also extends beyond the immediate team. Understanding the pressures that developers face when a deployment pipeline blocks them, or that a product manager faces when a release slips, allows DevOps engineers to design better processes and communicate with more genuine support rather than friction.

Putting It All Together

Technical skills—scripting, CI/CD tooling, cloud infrastructure, containerization, monitoring—remain the foundation of any DevOps career. But the practitioners who advance fastest and have the greatest impact tend to be those who pair their technical depth with the seven qualities above: self-directed learning, collaboration, communication, resilience, systems thinking, prioritization, and empathy.

If you’re evaluating your own readiness for a DevOps role—or building a DevOps team—this list is a useful starting point for honest self-assessment and structured interviews alike.

Quotes in this article were originally published by Forbes.

The Problem with Root Cause Analysis in a Microservices World

For decades, root cause analysis (RCA) has been the default framework for IT incident response. When an outage occurred in a monolithic application environment, teams would trace the failure back to a specific line of code, a failed switch port, or a faulty hard drive — one identifiable trigger that set off a domino effect. Fix the root cause, close the ticket.

That model is increasingly mismatched with how modern software is built. As applications decompose into microservices distributed across complex network infrastructure, the idea that a single root cause exists — and can be quickly identified during an active incident — breaks down. That was a central theme at the Velocity Conference 2017 in New York, where multiple DevOps engineers and engineering leaders called for a fundamental rethink of incident response philosophy.

Mitigation First, Diagnosis Later

Kristopher Beevers, founder and CEO of hosted DNS provider NS1, offered a direct reframing: focus on mitigation, not root cause, when a crisis begins.

“Identify and troubleshoot the service impact and worry about doing a diagnosis later.”

— Kristopher Beevers, NS1

This is a meaningful shift in priority ordering. Traditional RCA encourages responders to understand why something broke before restoring service. The emerging view separates those two activities: restore service first, investigate cause afterward — and accept that the investigation may not yield a single definitive answer.

Start at the Top of the Stack, Not the Bottom

Sarah Wells, principal engineer at the Financial Times, described a practical implementation of this philosophy. Her team has deliberately tuned out low-level infrastructure noise in favor of business-relevant service signals.

“We’ve turned off alerts for service-level errors, status codes and response times. We start [our analysis] at the top of the stack.”

— Sarah Wells, Financial Times

Her example was concrete: if the homepage list of top articles on the Financial Times website is broken, that is what matters to users and the business. That is where incident response should begin — not with a deep dive into infrastructure metrics that may have no direct relationship to visible service degradation.

This top-down approach aligns alert prioritization with user experience rather than system internals, which is especially valuable when system internals are themselves distributed across dozens or hundreds of microservices.

APIs as Boundaries of Responsibility and Observation

Mark McBride, founder of Turbine Labs and a former engineer at Twitter, Nest Labs, and Google, offered a structural argument for why microservice architectures naturally reduce the importance of traditional RCA.

At scale, members of the same IT organization become consumers of microservices offered by other teams via APIs. No single engineer understands everything behind every API their service calls. Nor do they need to — what they can observe and reason about is the behavior of the API itself: its latency, error rate, and response patterns.

This has two practical effects on incident response:

• Reduced anxiety: Individual responders are not expected to diagnose every upstream dependency. They own their API surface and can observe it directly.
• Common control plane: APIs provide a shared point of observation and remediation that spans different programming languages and runtimes, enabling global mitigations without requiring deep knowledge of every subsystem.

As McBride noted, this structure diminishes the need to find the specific element of infrastructure behind an API that malfunctioned — because the API boundary itself becomes the unit of recovery.

The Human Cost of Root Cause Analysis

Beyond the technical mismatch, several speakers highlighted a cultural problem with RCA: it encourages blame.

“It’s common for a human to be blamed as the root cause of a problem. Then companies end up firing people instead of improving things, and ‘cover your [butt]’ becomes the priority instead of solving problems.”

— Baron Schwartz, co-founder and CEO, VividCortex

When the answer to “what was the root cause?” becomes “an engineer made a mistake,” the process has failed organizationally even if it succeeded analytically. The resulting culture discourages transparency, slows learning, and pushes engineers toward self-protection rather than systemic improvement.

This is not a new observation — blameless postmortems have been a pillar of Site Reliability Engineering culture for years — but the Velocity speakers connected it directly to the argument against RCA as a primary incident response methodology.

From Postmortem to Incident Review: A Change in Language and Mindset

Peter Nealon, a solutions architect at Runkeeper (a mobile running app owned by ASICS), articulated the shift in post-incident analysis goals:

“You often don’t arrive at a single thing, and it’s more important to understand the context, contributing factors, and how to mitigate and remediate the issue.”

— Peter Nealon, Runkeeper

Nealon also flagged a symbolic detail: some DevOps engineers are abandoning the term postmortem in favor of incident review. The language is intentional.

“We’re learning how to improve versus figuring out what died and why. We aren’t aiming for root cause analysis, but to reduce our mean time to remediation.”

— Peter Nealon, Runkeeper

Mean time to remediation (MTTR) as the primary metric — rather than the ability to name a root cause — reorients the entire incident lifecycle toward business outcomes: restoring service quickly and preventing recurrence through systemic improvement.

Incident Commander Model: Structured Roles Under Pressure

Nealon described a concrete organizational change his team was implementing at the time: an incident commander role to direct troubleshooting during active incidents.

In the Runkeeper model:

• Application developers handle hands-on troubleshooting tasks, operating on the assumption that most incidents originate in application code rather than infrastructure.
• Site reliability engineers (SREs) act as subject-matter experts on the underlying platform, available to consult but not the default first responders.
• The incident commander coordinates communication, assigns tasks, and maintains situational awareness across the team.

This separation of concerns mirrors patterns from other high-stakes operational environments and reduces the cognitive load on any single responder during a crisis.

Key Takeaways for DevOps Teams

The arguments made at Velocity 2017 coalesce around a few actionable principles:

1. Prioritize service restoration over root cause identification during an active incident. Diagnosis can wait; user impact cannot.
2. Align alerting with business-critical services, not low-level infrastructure metrics. Start at the top of the stack.
3. Use API boundaries as units of observation and recovery in microservice environments. You do not need to diagnose what is behind an API to remediate through it.
4. Replace blame-oriented RCA with blameless incident reviews that focus on context, contributing factors, and systemic improvements.
5. Track mean time to remediation as the primary post-incident metric, not the ability to name a single root cause.
6. Define clear incident roles — such as an incident commander — to reduce chaos and duplicate effort during response.

These practices do not mean abandoning post-incident investigation entirely. They mean separating the when and the goal of that investigation: after service is restored, with the purpose of improving systems rather than assigning fault.

This article explains how to build a version control process for DevOps infrastructure, why Infrastructure as Code (IaC) is the foundation, and how to get started with Git and GitHub using a practical command-line walkthrough.

Why Infrastructure Needs Version Control

Infrastructure as Code (IaC) is the practice of defining servers, networks, storage, and other infrastructure resources in machine-readable configuration files rather than through manual processes or interactive consoles. IaC enables automation, testability, quality gates, and predictable deployments — the same benefits that application developers have enjoyed for years.

Storing IaC templates, shell scripts, and configuration artifacts in a version control repository brings additional benefits:

• Full audit history: Every change is recorded with a description, timestamp, and author, giving teams a clear point-in-time history from one revision to the next.
• Rollback capability: If a change breaks something, you can revert to any previous known-good state without guesswork.
• Collaboration without conflicts: Team members work from a shared central repository, merging changes in a controlled way rather than overwriting each other’s work.
• Automation triggers: Commits to a version control repository can automatically kick off pipelines — running tests, linting configuration files, or deploying to a development or staging environment.
• Shared understanding: Everyone on the team — developers, operations engineers, and security — can inspect a single source of truth for all resources that support a given application.

How Version Control Works for Infrastructure Teams

The mechanics of version control are the same whether you are tracking application code or an Ansible playbook. A central repository stores all files. Changes are tracked incrementally — each new commit layers on top of the previous state, preserving the full history. Team members synchronize a local copy of the repository, make changes, and merge those changes back to the central repository when ready.

This workflow reduces risk. Bugs introduced by a change can be isolated and reverted. Updates that break a deployment can be rolled back without affecting unrelated work. Multiple engineers can work on the same code base simultaneously, improving velocity without sacrificing consistency.

For infrastructure-focused engineers, adopting version control also means embracing software development habits: writing descriptive commit messages (e.g., “Applied security patch to base OS image referenced in Terraform module”), using branches for experimental changes, and submitting pull requests for peer review before merging to a main branch.

Choosing a Version Control System: Git and GitHub

Git is the most widely used version control system in the industry. It is distributed, meaning every engineer works with a full local copy of the repository — including its complete history — and can commit changes offline. Those commits are then pushed to a shared remote server when connectivity is available.

Git is open source, and a wide ecosystem of hosted platforms is built on top of it, including GitHub, GitLab, and Bitbucket. When selecting a platform, consider:

• Public vs. private repositories: Public repositories are visible to anyone. Infrastructure code almost always belongs in a private repository.
• Access controls: Fine-grained permissions determine who can read, write, or merge changes to protected branches.
• CI/CD integration: Most hosted Git platforms offer built-in pipelines or integrate with external tools such as Jenkins, GitHub Actions, or GitLab CI.
• Secrets management: Never store secrets, credentials, or API keys in a version control repository. Use a dedicated secrets manager and reference secrets by name in your IaC templates.

Practical Walkthrough: Git Version Control from the Command Line

The following walkthrough demonstrates the core Git version control workflow using the command line and a GitHub remote repository. The same workflow applies whether you are tracking a Terraform module, a Kubernetes manifest, or a CloudFormation template.

Step 1: Initialize a Local Repository

Open a terminal and navigate to the directory where you want to store your repositories. Create a new folder for this repository and initialize it with Git:

mkdir my-first-repo
cd my-first-repo
git init

Git responds with: Initialized empty Git repository in .../my-first-repo/.git/

Step 2: Add a File and Stage the Change

Create a README file. On GitHub, this file is rendered as the repository’s landing page:

echo "# my-first-repo" >> README.md

The file exists on disk but is not yet tracked by Git. Stage it to tell Git you want to include this change in the next commit:

git add README.md

Step 3: Commit the Change

Commit the staged change to the local repository with a descriptive message:

git commit -m "Initial commit: add README"

Write commit messages that describe why a change was made, not just what changed. For infrastructure code, this habit is especially valuable during incident investigation.

Step 4: Connect to a Remote Repository on GitHub

Sign in to GitHub and create a new private repository named my-first-repo. Then link the local repository to the remote:

git remote add origin https://github.com/[your-github-username]/my-first-repo.git

Push the local commits to the remote repository:

git push -u origin main

Other team members can now clone the repository to their own machines:

git clone https://github.com/[your-github-username]/my-first-repo.git

Step 5: Make a Change and Inspect the Diff

Edit README.md using any text editor. Add a line of text describing what the repository contains. Check which files have been modified:

git status

Stage and commit the update:

git add README.md
git commit -m "Update README with repository description"

Push the change to GitHub:

git push -u origin main

Navigate to the repository on GitHub and view the latest commit. GitHub displays a split-screen diff showing what was added (highlighted in green) and what was removed (highlighted in red) between the two versions. This visibility is one of the most practical benefits of version control for infrastructure teams: you can immediately see what changed and when.

Step 6: Extend the Repository with Infrastructure Artifacts

With the workflow in place, add the infrastructure files you need to track: Terraform modules, CloudFormation templates, Ansible playbooks, Kubernetes manifests, shell scripts, and pipeline definitions. Follow the same pattern for every change:

1. Modify or add a file.
2. Stage the change with git add.
3. Commit with a descriptive message using git commit -m.
4. Push to the remote repository with git push.

Branching Strategies for Infrastructure Code

Many teams use multiple branches to manage infrastructure changes safely. A common pattern is:

• Main branch: Represents the production-ready state of all infrastructure. Direct commits are typically blocked; changes arrive via pull requests.
• Feature branches: Short-lived branches where engineers develop and test a specific change — for example, adding a new VPC configuration or updating an AMI reference.
• Environment branches: Some teams maintain separate branches for development, staging, and production environments, promoting changes upward through the chain after validation.

Pull requests serve as the gatekeeping mechanism. Before a change merges to the main branch, other engineers can review the diff, leave comments, and require that automated tests pass. This process catches errors in infrastructure code before they reach production.

Connecting Version Control to Automation

Version control becomes even more powerful when it triggers automation. A commit or a merge to a protected branch can automatically:

• Run linting and static analysis on infrastructure templates (e.g., terraform validate, cfn-lint, or ansible-lint).
• Execute integration tests against a temporary environment.
• Deploy the updated infrastructure to a development or staging environment.
• Notify the team of the outcome via a chat integration or email.

This automation loop is the foundation of a mature DevOps pipeline. The version control repository becomes the single trigger point for all downstream activity, eliminating manual handoffs and reducing the opportunity for human error.

IaC Tooling That Works Well with Version Control

Version control is tool-agnostic, but the following categories of IaC tooling are commonly stored in Git repositories:

• Provisioning tools: Terraform, AWS CloudFormation, Azure Resource Manager templates, Google Cloud Deployment Manager, and OpenStack Heat define the infrastructure resources themselves.
• Configuration management: Ansible, Chef, and Puppet manage the software and settings installed on provisioned servers. These artifacts complement IaC provisioning templates and belong in the same repository or a linked one.
• Container and orchestration manifests: Kubernetes YAML files, Helm charts, and Docker Compose files define how applications run on the infrastructure.

Public and private cloud platforms — including AWS, Microsoft Azure, and Google Cloud Platform — provide APIs that these tools use to provision resources consistently and repeatedly. With cloud APIs and IaC templates version-controlled in Git, infrastructure deployments become as repeatable and auditable as application deployments.

Summary

Building a version control process for DevOps infrastructure is not complicated, but it does require a deliberate shift in how infrastructure teams work. The steps are straightforward: adopt Infrastructure as Code, store all artifacts in Git, follow a consistent commit-and-push workflow, use branches and pull requests to gate changes, and wire the repository to an automated pipeline.

The result is infrastructure that is auditable, reversible, collaborative, and automated — the same properties that software development teams have relied on for decades, now applied to the systems that run your applications.

The Identity Problem at the Heart of DevOps

For many organizations, choosing between hiring a DevOps engineer and a full-stack developer feels like a high-stakes decision with no clear answer. Part of the difficulty is definitional: DevOps remains one of the most widely discussed yet inconsistently defined concepts in modern software delivery.

Conventional hiring guidance tends to fall back on heuristics: “If your stack is stable and you’re not launching new products, you don’t need DevOps.” Or: “If you run a complex, multi-component architecture, DevOps is essential.” These rules of thumb aren’t wrong, but they’ve grown increasingly insufficient as the pace of change accelerates across nearly every industry.

Today, it’s hard to name a business of any meaningful size — regardless of industry — that isn’t pursuing growth, adopting more sophisticated technology, or trying to ship software faster. The context in which these hiring decisions are made has shifted. The question is no longer just which role to hire; it’s whether the traditional boundary between the two still holds.

Cut from the Same Cloth

When you examine the two roles closely, the similarities are striking. Both full-stack developers and DevOps engineers exist because businesses need people who can operate across traditional boundaries — between front-end and back-end, between development and operations. Both roles emerged in response to the same pressure: the demand for greater agility and broader technical coverage from fewer specialists.

This shared origin is reflected in compensation. DevOps engineers command competitive salaries across companies of different sizes, a signal that organizations recognize the strategic value of bridging development and operations. Full-stack developers are similarly well-compensated, particularly in consulting contexts where their ability to reason across an entire technology stack provides immediate business value.

What businesses are really paying for in both cases is range. A candidate with a broad, overlapping Venn diagram of skills — someone who can write application code, understand infrastructure, and reason about deployment pipelines — is worth more than a deep specialist in a single layer. The rise of DevOps culture has reinforced this, embedding agility and systems thinking into developer expectations more broadly.

Trading DevOps Tools: Where the Overlap Gets Concrete

Beyond the conceptual similarities, there is tangible evidence that the toolchains of full-stack developers and DevOps engineers are converging. Consider how web developers are increasingly adopting technologies traditionally associated with infrastructure and deployment:

• Docker — Originally an ops-centric containerization tool, Docker has become a standard part of many web development workflows. Developers use it to ensure consistent local environments and to package applications for deployment.
• Vagrant — Used to create reproducible, portable development environments, Vagrant blurs the line between local development and production infrastructure concerns.
• CI/CD tooling — Tools like Jenkins, GitHub Actions, and GitLab CI are increasingly expected knowledge for developers, not just DevOps engineers.
• Scripting languages — Bash, Python, and Perl — long associated with sysadmin and ops work — now appear regularly in full-stack developer job descriptions.

Packt’s 2017 Skill Up report (cited in the original source article) noted that Docker ranked as the top tool respondents planned to learn that year, with significant interest from developers across web development disciplines. This suggests that developers were actively choosing to expand into infrastructure-adjacent skills — not because job descriptions required it, but because they recognized the professional value.

What This Means for Developers and Hiring Teams

The practical implication is straightforward: the scope of what a “developer” is expected to know has expanded. Modern software delivery often requires the same person to write application code, containerize it, configure a CI pipeline, and understand how it will behave in production. Organizations that write job descriptions asking for JavaScript, Bash, CI tooling, and container knowledge are no longer the exception — they reflect where the industry is heading.

For developers, this isn’t necessarily a threat. It is an opportunity to increase market value by developing infrastructure literacy. Understanding how your application is deployed, monitored, and scaled makes you a more effective engineer regardless of your job title.

For hiring teams, the DevOps-engineer-versus-full-stack-developer framing may be less useful than asking: What specific problems do we need solved, and what combination of skills addresses them? In many cases, the answer will point toward engineers who operate fluidly across both domains — what some call a “T-shaped” or even “polyglot” engineer.

The Bigger Picture: Complexity as Opportunity

The convergence of these roles reflects a broader truth about DevOps: at its core, it is about breaking down silos and improving communication across the full software delivery lifecycle. Whether someone carries the title of DevOps engineer or full-stack developer matters less than whether they can contribute meaningfully across that lifecycle.

The increasing complexity of modern software stacks need not be a source of confusion or anxiety. For engineers willing to invest in expanding their skill sets — and for organizations willing to support that growth — it represents a significant competitive advantage. The most valuable technical professionals of the coming years will be those who can navigate the full journey from code commit to production with confidence.

That, ultimately, is what both DevOps and full-stack development have always been pointing toward.

Survey Background: Skill Up 2017 at a Glance

Packt’s annual Skill Up survey aims to help developers and technical professionals stay current by tracking which tools, technologies, and trends are gaining traction across the industry. The 2017 edition gathered input from approximately 5,000 respondents worldwide, covering topics from tooling preferences to salary benchmarks and career development.

In 2016, respondents identified Virtual & Augmented Reality, Machine Learning, and the Internet of Things as the biggest upcoming trends. By 2017, the picture had shifted: Machine Learning, Big Data, and Cloud Computing moved to the top of the list. Cloud Computing was particularly championed by Full-Stack Developers and Systems Administrators.

Salary Landscape: Who Earns the Most in Tech?

The survey shed light on compensation across different technical roles. Some results were predictable; others were more surprising.

Top Five Earners

1. C-Suite Level Managers
2. Big Data Engineers
3. Mid-Level Leads / Managers
4. Security Engineers
5. Information Architects

Cloud Engineers and DevOps Engineers ranked 6th and 7th respectively — above the industry average, but not at the very top. For a field that demands broad expertise across infrastructure, automation, and software delivery, the relatively high but not top-tier compensation for DevOps Engineers reflects how competitive and in-demand these skills have become.

Lowest-Paid Roles

1. Hobbyists
2. Game Developers
3. Web Developers
4. Technical Support
5. Mobile Developers

It is notable that Game Developers and Mobile Developers — roles often perceived as glamorous or cutting-edge — appear near the bottom of the earnings table. This likely reflects highly saturated job markets and a large proportion of independent or freelance workers in those categories.

Skills That Command the Highest Salaries

When respondents were asked which specific technical skills were associated with the highest-paying roles, the results highlighted a mix of data engineering, operations tooling, and analytics platforms. The top five skills linked to the best compensation were:

1. Splunk
2. Hadoop
3. Kafka
4. Chef
5. SAS

For DevOps practitioners, it is encouraging to see Hadoop and Chef in the top five. Hadoop signals the growing overlap between DevOps and Big Data engineering, while Chef’s presence confirms that configuration management expertise remains financially rewarding. Splunk’s top ranking reflects strong enterprise demand for log management and operational intelligence skills.

Most Popular Tools Across the Industry

Across all respondents, the three most widely used tools in 2017 were Python, Git, and Visual Studio. However, tool preferences diverged significantly by role:

• DevOps Engineers: Favored Docker over Visual Studio, using Python and Git alongside it.
• Cloud Engineers: Cited Python, Docker, and AWS as their primary tools.
• Information Architects: Preferred Python, Eclipse, and Visual Studio.

Docker’s prominence across both DevOps and Cloud Engineering roles underscores how container technology had already become a foundational part of modern infrastructure workflows by 2017.

The Top Tools to Learn Next: Docker, Ansible, and Kubernetes

When respondents were asked which tools they planned to learn over the next 12 months, the findings provided a clear signal for where DevOps careers were heading.

Across all respondents, the top three tools to learn were:

1. Docker
2. Python
3. Angular

For DevOps Engineers specifically, the priorities were even more focused on container and automation tooling:

1. Docker — Container runtime and image management
2. Ansible — Agentless configuration management and automation
3. Kubernetes — Container orchestration at scale

Cloud Engineers showed closely aligned priorities, but swapped Docker for OpenStack as their primary focus — reflecting a stronger emphasis on private and hybrid cloud infrastructure provisioning. Information Architects prioritized Docker, Hadoop, and React.

Why Docker, Ansible, and Kubernetes Still Matter

Although this survey data comes from 2017, the three tools DevOps engineers identified as critical learning priorities have only grown in importance since then. Understanding why professionals prioritized them helps explain the direction of modern infrastructure engineering:

Docker

Docker standardizes how applications are packaged and run by isolating them in lightweight containers. It removes environment inconsistencies between development, testing, and production — one of the most persistent pain points in software delivery. Its consistent image format also made it the natural starting point for any container-based workflow.

Ansible

Ansible’s agentless architecture and human-readable YAML playbooks made it one of the most accessible configuration management and automation tools available. It integrates well with cloud providers, container runtimes, and CI/CD pipelines, making it a practical choice for teams of any size.

Kubernetes

Kubernetes (K8s) addresses the challenge of running containers at scale — scheduling workloads, managing service discovery, handling rolling deployments, and maintaining desired system state. Its growing adoption by major cloud providers (Google GKE, AWS EKS, Azure AKS) confirmed its position as the de facto container orchestration standard.

Key Takeaways for DevOps Professionals

• Machine Learning, Big Data, and Cloud Computing were identified as the dominant industry trends in 2017, reflecting where engineering investment and hiring demand were concentrating.
• DevOps and Cloud Engineers rank among the higher-earning technical roles, though not at the very top of the pay scale.
• Configuration management (Chef, Ansible) and data platform skills (Hadoop, Kafka, Splunk) were most strongly associated with higher compensation.
• Docker, Ansible, and Kubernetes form a coherent, complementary skill set: containerization, automation, and orchestration — the three pillars of modern cloud-native infrastructure.
• Python remains a universal skill valued across DevOps, Cloud, and development roles alike.

Source: Packt Skill Up 2017 Survey, as reported by JAXenter.