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As cloud adoption rises, so does cloud “sticker shock.” That tremendous savings seen from the switch to up-front CapEx investments in information technology to subscription mode soon gets soured as the rising monthly bills come in for services nobody knows where and when they are being used.

If you feel your organization is behind the curve with automating even the most routine parts of cloud, and cloud spending is an unseen disaster waiting to happen, don’t feel so bad — even the most informed financial experts are still trying to figure these things out.

We’re talking about those who are part of a rising new discipline called “FinOps” — the practice of monitoring, measuring and mitigating the costs and value delivered from cloud. The perspectives of FinOps practitioners (yes, they are out there) provide a good look at understanding what lies ahead in cloud. Here, we see automation is still a struggle to fully attain, and cloud spending a big question mark.

These are the findings of the FinOps Foundation, a non-profit trade association focused on codifying and promoting cloud financial management best practices and standards, detailed in a recent survey of more than 800 FinOps practitioners from around the world with a collective $45 billion in annual cloud spend. “The dirty little secret of cloud spend is that the bill never really goes down,” says J.R. Storment, executive director of the FinOps Foundation.

The results show massive adoption of public cloud spend, and the struggle to contain and optimize cloud spend. Nearly half of survey respondents (49%) had little or no automation of managing cloud spend. Of those with some automation, almost one-third automated notifications (31%) and tagging hygiene (29%.) Only 13% automated rightsizing and 9% spot use. This “indicates that companies are likely missing opportunities to optimize cloud spend,” the survey’s authors note.

Half of compute spend on public cloud was for on-demand, the highest-price service; and 49% for reserved, savings or committed use coverage, the next-costliest option. Only 13% was for spot use, the least expensive service, even though respondents identified 28% as being an “excellent” target for that option.

Getting engineers to act on cost optimization was cited by 40% of respondents as the biggest challenge, followed by dealing with shared costs (33%) and accurate forecasting spend (26%.)

The most oft-used tools used for managing cloud costs include AWS Cost Explorer, Cloudability (Apptio), CloudHealth (VMWare), Azure Cost Management, GCP Cost Tools, and Cloudcheckr. About half, 46%, use cloud native tooling as their primary technology, 43% use a 3rd party platform, and 11% use home grown tools or spreadsheets. At the same time, many FinOps practitioners still rely on data collection, collation, and analysis via spreadsheet. Almost all practitioners use a combination of tooling, while still relying on spreadsheets for some tasks — with forecasting being the biggest Excel use.

The survey’s authors project that significant growth is ahead for FinOps, the field of cloud financial management, as more companies accelerate their cloud plans, especially amid COVID-19, and struggle to contain and optimize cloud spend. The survey respondents predicted an over 40% growth in FinOps team size in the next 12 months.

For the most part, FinOps is a part-time pursuit, and organizations need to buy into supporting efforts to monitor and manage cloud costs. Some challenges cited by respondents in the survey include the following:

Measuring real-time cost analysis between the various cloud providers.
Providing foundational education for engineering teams.
Having visualization of cloud architecture, versus cost of those parts of the infrastructure.
Facilitating robust tag enforcement, with easy ability to export cost and presentation to teams.
Enabling reporting total spend, cloud spend, service management spend and contract spend, the entire cloud spend picture.
Achieving container control, network and hardware integration at the scale and speed of cloud.”
Highlighting and fostering conversations around cloud cost management from a behavior change perspective.

This article breaks down the core responsibilities, required experience, technical skills, and soft skills that define the DevOps Engineer role today.

Where the DevOps Engineer Fits in the Team

The name DevOps describes the discipline’s central purpose: bridging the gap between software Development and IT Operations. Historically, these two groups operated in silos, each optimising for different goals — developers pushing for speed and change, operations teams prioritising stability. The DevOps Engineer acts as the connecting layer between them.

In practice, DevOps Engineers work with a wide range of internal stakeholders: QA personnel, application developers, architects, project managers, and business stakeholders. They rarely interact directly with external end-users, but they maintain a customer-first mindset to serve the needs of their internal clients effectively.

Key team responsibilities include:

• Liaising between development and IT operations teams to align on shared goals and workflows.
• Mentoring software developers and architecture teams on building scalable, deployable software.
• Collaborating with security teams to ensure quality, secure releases.
• Contributing to or overseeing DevSecOps practices where security is embedded into the development pipeline from the start.

While DevOps Engineers frequently work independently on focused tasks, they are expert collaborators by necessity. Their work builds on the output of development and operations colleagues, requiring clear, consistent communication across organisational boundaries.

Experience Required

Most DevOps roles require hands-on experience with integration technology, automation tooling, and cloud platforms. Professionals entering the role commonly come from backgrounds such as Systems Administration, IT Project Management, or Database Administration — though many paths are valid.

Typical job postings ask for at least two to three years of relevant experience. The following competency areas are commonly expected:

• Agile Project Management — understanding iterative delivery and sprint-based workflows.
• Cloud Technology — practical experience with one or more major cloud platforms (AWS, Azure, GCP).
• Container Concepts — working knowledge of Docker, container orchestration, and image management.
• Continuous Integration / Continuous Delivery (CI/CD) — building and maintaining automated build and deployment pipelines.
• Deployment and Infrastructure Automation — using tools such as Ansible, Terraform, Chef, or Puppet to codify and automate infrastructure provisioning.
• Orchestration — managing containerised workloads with tools such as Kubernetes.
• Open Source Operating Systems — solid Linux administration skills are typically expected.
• Source Control — proficiency with Git-based workflows, branching strategies, and code review processes.

Because DevOps Engineers spend much of their time extending and amending existing platforms rather than building new ones from scratch, breadth of knowledge across these areas is often valued as much as depth in any single technology.

Infrastructure Planning, Testing, and Development

Day-to-day, a DevOps Engineer looks at specific functions within a cloud environment and writes code to adapt them to immediate business needs. This might mean adding users or permissions to a cloud platform, modifying workflow processes, spinning up new containers, or adjusting configuration to meet a scaling requirement.

This work involves:

• Rapid, targeted coding — writing small, precise code snippets across multiple languages and configuration formats (e.g., YAML, Bash, Python, HCL).
• Testing in virtual environments — validating changes in staging or sandbox environments before they reach production.
• Documentation — recording configuration decisions, runbooks, and architectural diagrams so teams can maintain and extend the infrastructure.
• Optimisation and support — monitoring performance and reliability, and tuning infrastructure components over time.

Crucially, a strong DevOps Engineer does not lose sight of the bigger picture. Infrastructure planning — understanding how systems integrate across an organisation and where future scaling pressure will emerge — is one of the skills that distinguishes a DevOps Engineer from a narrower infrastructure administrator role.

Security and Automation

DevSecOps: Security as a First-Class Concern

Writing secure code is not optional in modern DevOps — it is a core responsibility. DevOps Engineers are accountable for the security posture of the infrastructure they build and maintain. This means building security controls into software and infrastructure from the earliest stages of development rather than bolting them on afterwards.

This approach is formalised in the DevSecOps model, where security practices are integrated directly into the CI/CD pipeline. A DevSecOps team (which the DevOps Engineer may lead or closely collaborate with) typically:

• Scans code and dependencies for known vulnerabilities as part of automated pipelines.
• Performs continuous monitoring across all platforms for anomalous behaviour.
• Defines and enforces security policies as code (e.g., using tools like Open Policy Agent).
• Educates development and operations colleagues on secure coding and configuration practices.

Automation: The Core Differentiator

Automation expertise is arguably the single most important technical differentiator of the DevOps Engineer role. Where other IT roles may rely on manual processes for provisioning, configuration, testing, and deployment, DevOps Engineers systematically identify those manual steps and replace them with repeatable, auditable automated workflows.

The practical benefits are significant: automated pipelines reduce human error, accelerate deployment frequency, and free engineering time for higher-value work. Staying current with automation tooling — and knowing when a new tool genuinely improves a workflow versus adding complexity — is an ongoing responsibility for anyone in this role.

Important Soft Skills

Technical depth alone is not enough to succeed as a DevOps Engineer. The role sits at the intersection of multiple teams and disciplines, which demands a specific set of interpersonal and organisational skills:

• Communication — DevOps Engineers must translate technical constraints and requirements clearly across audiences: from developers and architects to project managers and business stakeholders. Ambiguity in this role has downstream consequences.
• Liaison and diplomacy — Finding common ground between development teams (focused on shipping features) and operations teams (focused on stability) requires patience and the ability to frame shared goals.
• Anticipating needs — Effective DevOps Engineers develop an intuition for where an organisation’s technology gaps will emerge before they become incidents. Understanding how people, culture, and processes interact is part of diagnosing what tooling or workflow changes will actually help.
• Teaching and mentoring — Because DevOps knowledge is still unevenly distributed across many organisations, DevOps Engineers frequently educate colleagues on topics such as scalability, security, and automation best practices.

The Outlook for DevOps Engineers

Organisations that have successfully adopted DevOps practices report improvements in deployment frequency, lead time for changes, and overall delivery reliability. Beyond the operational metrics, DevOps has had a measurable cultural impact in many companies — shifting responsibility for reliability and quality from a single team to a shared organisational value.

Despite this growth, hiring managers continue to find the role difficult to define precisely. Because DevOps Engineers can come from many different backgrounds and the toolchain changes rapidly, job descriptions vary widely. The consistent signals across strong candidates are:

• Proven experience with deployment automation and CI/CD pipelines.
• Hands-on infrastructure automation skills (infrastructure as code).
• Solid version control and Git workflow knowledge.
• A collaborative, communication-forward working style.

For engineers considering a move into DevOps, the most effective approach is to build practical experience across the technology areas listed above — ideally through real project work — while developing the cross-functional communication skills the role demands.

Original source material: bmc.com

Cloud Adoption Is Widespread, but Deep Value Remains Elusive

Despite cloud computing being mainstream for well over a decade, many organizations are still treating it as little more than a remote data centre — a place to park virtual machines (VMs) rather than a platform for genuine transformation. The author, writing from more than ten years of hands-on experience as a CIO and CTO, argues that this conservative posture leaves the most important benefits of cloud computing completely untapped.

Case Study: Elastic Donation Infrastructure at Comic Relief

The author’s first significant encounter with cloud’s transformational potential came in 2009 while serving as CIO of Comic Relief, the UK charity known for its televised fundraising events (Red Nose Day and Sport Relief). The organisation faced a classic traffic-spike engineering problem: its telethons, watched by millions, generated enormous bursts of online donation traffic concentrated in a very short window on broadcast night. Building permanent on-premises infrastructure capable of sustaining hundreds of transactions per second — for only one or two peaks per year — would have been financially unsustainable.

Working with major technology partners, the team built a horizontally scalable, cloud-based donation platform. Horizontal scalability in this context meant that capacity could be increased by adding additional compute nodes during peak demand, rather than replacing or upgrading existing hardware. Nodes could be provisioned cheaply and quickly, and decommissioned once the peak had passed. The result was an autoscaling system that set transaction-volume records while keeping infrastructure costs proportionate to actual usage — an early, practical proof of what cloud elasticity could deliver.

Case Study: Migrating the National Geographic Image Collection to Google Cloud

Later, as CTO at National Geographic in Washington, D.C., the author led the migration of the organisation’s image collection — approximately two million iconic photographs, including the famous “Afghan Girl” portrait — from an internal on-premises platform to Google Cloud.

The migration addressed two distinct problems:

• Performance: The legacy on-premises system could no longer keep pace with user expectations as concurrent session counts grew. Moving to Google Cloud provided cost-effective access to additional compute capacity, improving application responsiveness at scale.
• Security: The existing system relied on a simple username-and-password authentication model that was out of alignment with modern security policies. The migration enabled the team to implement a new identity and access management (IAM) framework using Google Cloud’s native capabilities, restricting access to properly authenticated users and significantly improving the overall security posture.

The National Geographic case illustrates that cloud migration, when approached thoughtfully, is an opportunity to remediate technical debt in security and architecture — not just to replicate existing infrastructure in a different location.

Why Most Cloud Migrations Underperform

Enterprise cloud adoption, while growing, remains shallower than headline figures suggest. Even organisations that have formally adopted cloud services often have only a small fraction of their total workloads running off-premises. Two structural barriers explain much of the gap between cloud’s promise and its delivered value.

1. Hidden Transition Costs

The cost savings cited by cloud providers are real over the long term, but they are not automatic. Organisations frequently encounter an initial increase in costs during and immediately after migration, driven by factors including:

• Investment in retraining existing staff in cloud-native tools and practices
• New operational processes required for cloud environments — revised security policies, VPN configurations, access controls
• Data residency and sovereignty laws in certain jurisdictions that restrict where data can be stored or processed
• A shift from capital expenditure (CapEx) for hardware purchases to operational expenditure (OpEx) for cloud consumption — a change that can disrupt established budgeting and financial approval processes

2. Legacy Application Architecture

Many enterprise applications were designed within a data-centre paradigm: large, monolithic codebases running on tightly coupled hardware and middleware. These architectural characteristics directly limit the ability to exploit cloud-native capabilities. For example, decomposing a monolith into microservices and well-defined APIs allows separate teams to develop, test, and deploy individual components independently, reducing deployment bottlenecks and enabling faster iteration. A monolith moved to the cloud as-is gains little from that decomposition opportunity.

This is why a pure “lift and shift” strategy — moving VMs and their workloads to cloud infrastructure with no architectural changes — frequently fails to deliver meaningful improvements. It relocates existing constraints rather than resolving them.

The Alternative: A “Move and Improve” Mindset

Rather than treating migration as a destination, organisations should treat it as the beginning of a modernisation journey. The author proposes a four-phase framework for progressive cloud transformation.

Phase 1: Audit and Rationalize

Before migrating anything, take stock of what you actually have and whether it is worth migrating. Key questions to answer at this stage:

• Which applications are mission-critical, and which are candidates for deprecation?
• Could a legacy custom-built application be replaced entirely by a modern SaaS product, avoiding a complex migration altogether?
• What are the dependencies between systems, and in what order should workloads move?

A structured audit prevents teams from investing effort migrating applications that should be retired or replaced — a common and expensive mistake.

Phase 2: Optimize

Once the migration roadmap is clear, identify immediate improvements that the move to cloud makes possible. Security is often the most accessible quick win: replacing a weak authentication model with cloud-native IAM, enforcing least-privilege access, and enabling audit logging are improvements that can be implemented during migration without requiring significant changes to application code.

Phase 3: Enhance

Look for components of the application — or services that support it — that can be upgraded independently, without a full rewrite. Examples include:

• Replacing an aging relational database with a cloud-native managed database service, reducing licensing costs and operational overhead
• Improving front-end performance to increase conversion rates for customer-facing applications — quantifying the revenue impact of performance improvements helps build the business case
• Offloading static asset delivery to a cloud CDN to reduce latency globally

The enhancement phase is most effective when guided by the key business metrics that matter most to the organisation. Start with the pain points that have measurable financial or operational consequences.

Phase 4: Modernize

With a stable, optimised cloud-hosted application as the foundation, teams can begin more fundamental architectural changes. This is where cloud-native capabilities deliver their greatest leverage:

• Decomposing monolithic applications into microservices and APIs, allowing independent deployment of discrete components and significantly increasing development velocity
• Replacing rule-based or manual processes with machine-learning services — for example, automating metadata tagging in a document management system, with human review reserved for edge cases
• Adopting managed services for infrastructure-heavy concerns (queuing, caching, storage, identity) so that engineering effort shifts toward product features rather than platform maintenance

Lift and Shift Is a Starting Point, Not a Strategy

Moving VMs to the cloud can be a legitimate first step — it establishes connectivity, builds team familiarity with cloud operations, and may deliver some immediate cost relief on ageing hardware. But it should be understood as a foundation, not a finished state. The durable value of cloud computing for an organisation comes from progressively modernising the applications and processes that run on top of it.

For technology leaders, this means framing cloud adoption internally not as an infrastructure project but as a business transformation programme — one that requires sustained investment in people, process, and architecture, and that delivers compounding returns over time rather than a single cost-reduction event.

Key Takeaways for DevOps and Platform Teams

• Elasticity is the killer feature: If your cloud architecture cannot scale dynamically in response to demand, you are not exploiting the fundamental advantage cloud provides over on-premises infrastructure.
• Audit before you migrate: Not every workload deserves to be migrated. Replace or retire where possible; only migrate what genuinely needs to move.
• Security is not optional: Migration is an opportunity to raise your security baseline. IAM, secrets management, and zero-trust network access should be part of every cloud project scope.
• Decompose incrementally: You do not need to rebuild everything as microservices on day one. Identify bounded contexts within existing monoliths and extract them progressively.
• Measure business outcomes: Cloud projects that cannot demonstrate impact on revenue, cost, or risk will lose executive support. Instrument early, measure continuously, and communicate results clearly.

Where Is Ansible Used?

Before exploring the career benefits, it helps to understand where Ansible fits in a modern infrastructure. Ansible is primarily used to automate three categories of work:

• Configuration management: Changing OS settings, managing services, and installing or updating software across many hosts simultaneously.
• Provisioning: Spinning up and configuring the servers, virtual machines, or cloud instances that make up your infrastructure.
• Application deployment: Pushing application code to production environments in a consistent, repeatable way.

Ansible certification programmes typically cover both fundamentals and advanced topics, including Playbooks, Roles, Loops, Conditionals, Inventory management, and integration with tools such as Vagrant. That breadth of coverage makes a structured course a useful shortcut compared to purely self-directed learning.

1. Access to High-Paying DevOps Roles

DevOps engineers who can demonstrate automation skills — particularly around configuration management and infrastructure provisioning — consistently command competitive salaries in the tech job market. Ansible proficiency is a commonly listed requirement in DevOps, Site Reliability Engineering (SRE), and Platform Engineering job postings.

Because Ansible covers configuration management, provisioning, and deployment in a single tool, a practitioner who knows it well can take ownership of large portions of the automation pipeline. That scope of responsibility translates directly into stronger negotiating leverage when discussing compensation or promotion.

2. Increased Personal and Team Productivity

Repetitive manual tasks — patching servers, deploying releases, configuring new environments — are a significant drain on engineering time. Ansible lets you define those tasks once in a playbook and execute them reliably across any number of hosts. Common productivity wins include:

• Consistent environment setup that eliminates “works on my machine” problems.
• Parallel execution across large server fleets without proportionally more effort.
• Automated provisioning of IoT devices or cloud instances at scale.
• Reduced infrastructure management overhead, freeing budget and time for higher-value work.

When you can demonstrate measurable time savings through automation, that contribution is visible to leadership — which directly supports career progression.

3. Leverage a Large, Active Open-Source Community

Ansible is one of the most active open-source projects in the infrastructure automation space. A large community means a wealth of publicly available roles on Ansible Galaxy, active forums, detailed documentation, and a broad base of colleagues and mentors to learn from.

For someone early in their automation career, this community effect is significant. Rather than building everything from scratch, you can study well-maintained community roles, contribute improvements, and build a public portfolio that is visible to potential employers. Active community participation also keeps your skills current as the tooling evolves.

4. Agentless Architecture Lowers the Barrier to Adoption

Unlike some automation tools that require an agent process running on every managed node, Ansible communicates over SSH (or WinRM for Windows hosts). There is nothing to install on target servers before you can manage them. This agentless design has several practical career implications:

• Fast time to value: You can demonstrate results in an organisation quickly, without a lengthy bootstrapping phase.
• Easier adoption: Security and operations teams are often more comfortable granting SSH access than approving a persistent agent binary.
• Simpler troubleshooting: Fewer moving parts means fewer failure modes to diagnose.

Being able to introduce automation with minimal organisational friction is a skill in itself — and one that makes an Ansible practitioner visibly useful in almost any IT environment.

5. Low Learning Curve, High Practical Ceiling

Ansible playbooks are written in YAML, a human-readable format that does not require deep programming experience to get started. An IT professional comfortable with the command line can typically write and run a basic playbook within a day. That low entry barrier means:

• You can apply Ansible skills quickly on real projects rather than spending months on theory.
• System administrators without a software development background can automate tasks that previously required scripting expertise.
• The same playbook logic scales from managing a single server to hundreds with no structural change.

The ceiling is equally high. Advanced practitioners use Ansible with dynamic inventory, custom modules written in Python, complex Jinja2 templating, and integration into CI/CD pipelines — providing a clear path for continued skill growth long after the basics are mastered.

Going Further: Ansible Automation Platform

Red Hat’s Ansible Automation Platform (which includes the controller previously known as Ansible Tower) provides a graphical interface, role-based access control, scheduling, and centralised logging on top of core Ansible. Familiarity with the platform is increasingly expected in enterprise DevOps roles, making it a worthwhile extension of foundational Ansible training.

Getting Started

If you are ready to build Ansible skills, look for courses that cover the full workflow: writing playbooks, organising code with Roles, managing inventory, using Variables and Vault for secrets, and integrating Ansible into a CI/CD pipeline. Hands-on labs with real infrastructure — even a local Vagrant or Docker setup — will accelerate learning far more than video content alone.

The combination of broad industry demand, a genuinely approachable learning curve, and deep practical capability makes Ansible one of the higher-return skills available to DevOps and infrastructure professionals today.

The Management Track Isn’t the Only Track

A persistent myth in software engineering is that career advancement means moving into management. Senior developer becomes team lead, team lead becomes engineering manager, and so on up the org chart. For developers who genuinely want to lead people, that path makes sense. But for many skilled engineers, it’s a trap.

Many developers chose programming precisely because they want to be individual contributors — people who solve hard technical problems, not people who schedule one-on-ones and track sprint velocity for others. Some enjoy mentoring junior colleagues, but mentoring is not the same as managing. Forcing the distinction on people leads to predictable outcomes: reluctant managers, frustrated teams, and slower product delivery.

Why Pushing Developers Into Management Backfires

The pressure to move into management comes from multiple directions. Developers sometimes fear that staying in an individual contributor role means hitting a career ceiling — in title, influence, or compensation. On the other side, engineering leaders sometimes promote top performers into management to free up budget for less expensive junior hires, which is a short-sighted strategy that often destroys the very expertise it was meant to leverage.

The cycle that follows is familiar. A developer is promoted to project lead. Instead of deepening their technical skills, they find themselves running status meetings and chasing teammates for updates. Their own coding output drops. Their sense of professional value — previously grounded in shipping good software — erodes. They either burn out, quietly revert to individual contributor work, or leave the company entirely.

None of those outcomes serve the team or the organization. The good news is that developers don’t have to choose between stagnation and reluctant management. There are at least three well-defined, high-impact career paths that don’t require becoming a people manager.

3 Career Paths for Developers Who Don’t Want to Manage People

1. Software Architect

For developers who are deeply technical and want to remain that way, the software architect role is often the natural destination. Titles vary by organization — principal engineer, systems architect, distinguished engineer — but the core responsibility is consistent: making high-level, consequential decisions about how software systems are designed, structured, and evolved.

Architects typically do not have direct reports. They are senior problem solvers who influence the work of entire teams without managing those teams in the traditional HR sense. Many bring a decade or more of hands-on experience, either as deep specialists in a particular domain or as generalists with meaningful exposure across multiple technology stacks.

Architects also frequently mentor junior and mid-level developers — reviewing designs, explaining tradeoffs, and setting technical standards — but they do so as technical leaders, not as line managers.

How to support developers on this path:

• Encourage continued learning across the full software development stack, even for specialists.
• Give them ownership of meaningful architectural decisions on real projects.
• Support deep expertise in at least one or two languages or platforms, while maintaining broader technical literacy.
• Involve them in design reviews and system-level planning sessions early in their development.

2. Lead Developer (Technical Team Lead)

The lead developer role sits at the boundary between technical and organizational work. It suits developers who have strong interpersonal skills, enjoy coordination, and want to stay close to the codebase while also taking on some people-oriented responsibilities. This is not a purely managerial role — depending on the organization, it can involve a significant amount of hands-on coding alongside delegation and communication work.

Lead developers act as a bridge: they translate business requirements into technical direction for their teams, and they surface technical realities to nontechnical stakeholders. They typically do have direct reports, which distinguishes this path from the architect role.

The critical point is that people management requires a genuinely different skill set than coding. Writing great software and effectively guiding a team of engineers are distinct disciplines. Developers considering this path should enter it with clear eyes about that difference.

How to support developers on this path:

• Assign small, self-managed projects first. Can they hold themselves accountable to deadlines without external pressure?
• Gradually introduce delegation. Can they communicate work requirements clearly and evaluate others’ output fairly?
• Provide explicit training or coaching in people management — don’t assume technical excellence transfers automatically.
• Make the role’s balance between coding and management explicit upfront so there are no surprises.

3. Senior Technical Project Manager

Less common than the first two, but a legitimate path for the right developer, is the senior technical project manager role. This position suits developers who have developed strong skills in planning, resource allocation, budgeting, and stakeholder communication — and who find that kind of orchestration genuinely satisfying.

Technical project managers keep complex projects on schedule and on budget. They work closely with both engineering teams and business stakeholders, and their technical background gives them a credibility advantage that a nontechnical project manager won’t have. They understand what engineers mean when they describe a problem, and they can make more informed tradeoff decisions as a result.

Strong candidates for this role tend to be organized, adaptable under pressure, enthusiastic about coordinating complex moving parts, and comfortable operating in ambiguity.

How to support developers on this path:

• With appropriate oversight, let them plan projects: scope, timeline, resource allocation.
• Arrange for them to shadow an experienced project manager on a real engagement.
• Encourage pursuit of formal project management frameworks (such as Agile, Scrum, or PMP preparation) where relevant.
• Look for natural signals: developers who proactively track project status and flag risks are already showing the instincts this role requires.

The Manager’s Role: Guide, Don’t Push

One of the most important — and most frequently mishandled — parts of engineering leadership is helping developers understand what their options actually are. Many developers default to the management track not because they want it, but because they don’t know another path exists or because their organization’s reward structures make individual contributor roles feel like dead ends.

Engineering leaders can change this by being explicit about alternate paths, by ensuring that senior individual contributor roles carry comparable compensation and recognition to management roles, and by observing their developers carefully enough to suggest paths that fit their actual strengths and interests.

What leaders should avoid is pushing. Pressure-promoting a talented developer into a role they don’t want doesn’t serve the developer, the team, or the organization. Technical teams are most effective when the people in them are doing work they find meaningful — and when the structure around them makes that possible.

Summary

The right path depends on the individual. The job of an engineering leader is to make sure developers know all three exist — and to build an organization where each one is genuinely respected.

What Is Continuous Delivery?

Continuous Delivery is a software engineering discipline in which teams build software in small, frequent increments and ensure that the codebase is always in a releasable state. Unlike a big-bang release cycle, CD keeps the path from code commit to production short, automated, and repeatable.

The core idea is straightforward: automate everything that can be automated—builds, tests, environment provisioning, and deployment—so that releasing software becomes a low-stress, on-demand activity rather than a high-risk event. CD differs from Continuous Deployment in one important way: a human still approves the final push to production, which gives teams control without sacrificing speed.

High-performing engineering organisations at companies such as Amazon, Google, Facebook, and Etsy have long practised variants of Continuous Delivery, shipping code to production many times per day. For developers at smaller organisations, demonstrating fluency with CD principles and tooling is increasingly a differentiator in hiring and promotion decisions.

How Continuous Delivery Advances a Developer’s Career

1. Position Yourself for the Best Projects

Junior and mid-level developers frequently find themselves stuck maintaining brittle deployment scripts, chasing environment approvals, or acting as a manual handoff point between development and operations. This kind of work is necessary, but it rarely leads to recognition or growth.

Continuous Delivery replaces those manual, error-prone steps with automated pipelines. When deployments no longer require a developer’s manual intervention, that developer is free to contribute to new features, architectural improvements, and the projects that get noticed. Mastering CD is therefore not just about doing your current job better—it is about creating space to do higher-value work.

2. Increase Day-to-Day Job Satisfaction

One of the most common sources of developer burnout is the weight of tedious, repetitive work: late-night deployments, emergency patches caused by infrequent large releases, and long waits for environment availability. Continuous Delivery directly reduces this friction.

• Fewer emergency deployments. Small, frequent releases mean each change is easier to test and roll back, so production incidents caused by large, unexpected changesets become less common.
• No more waiting on environment provisioning. Infrastructure-as-code and automated environment setup, both prerequisites of a mature CD pipeline, eliminate the bottleneck of requesting and waiting for test environments.
• Less approval overhead. Automated quality gates—unit tests, integration tests, security scans, performance benchmarks—replace slow, manual sign-off processes.
• Faster feedback loops. Code merged today can be in front of real users within hours, giving developers actionable feedback rather than speculation.
• More time for meaningful engineering work. When the mundane is automated, developers can focus on problem-solving, system design, and technical depth—the work most engineers entered the profession to do.

3. Build Better Products Through Validated Learning

Continuous Delivery makes it practical and economical to release in small batches and gather real user feedback at every step. This tight feedback loop fundamentally changes how products are built.

Techniques such as A/B testing and feature flags, which are much easier to implement with a CD pipeline already in place, allow teams to validate assumptions before committing to full feature development. Rather than building a complete feature over several months and discovering it does not deliver value, a team can ship a minimal version, measure its effect, and decide whether to invest further. This reduces wasted engineering effort and leads to products that users actually want.

For developers, this matters for career growth: being associated with products that succeed—and understanding why they succeed—is a much stronger career signal than shipping code into a void and hoping for the best.

4. Develop a Reputation for Innovation

Creativity in software engineering requires two things: the opportunity to work on forward-looking projects and the time to do so. Continuous Delivery creates both. By reducing the operational burden on developers, it frees cognitive bandwidth for experimentation and invention.

Beyond that, understanding CD principles—pipeline design, deployment strategies such as blue-green or canary releases, observability, and feedback mechanisms—signals to employers that a developer thinks in systems, not just in isolated tasks. This is precisely the profile that engineering managers look for when staffing ambitious projects or building out platform and DevOps teams.

Core Skills to Develop Alongside Continuous Delivery

To benefit fully from Continuous Delivery as a career practise, developers should build competence in the following areas:

• Automated testing. CD depends on a reliable, fast test suite. Unit, integration, and end-to-end testing skills are foundational.
• CI tooling. Familiarity with tools such as Jenkins, GitHub Actions, GitLab CI, or CircleCI is expected in most modern engineering roles.
• Infrastructure as Code. Tools such as Terraform, Ansible, or Pulumi allow environments to be version-controlled and provisioned automatically.
• Containerisation and orchestration. Docker and Kubernetes are now standard building blocks of CD pipelines.
• Observability. Logging, metrics, and distributed tracing skills help developers understand system behaviour after deployment and respond quickly when something goes wrong.
• Deployment strategies. Understanding blue-green deployments, canary releases, and feature flags allows teams to manage risk during rollouts.

Getting Started

If your team does not yet practise Continuous Delivery, the most practical entry point is improving your automated test coverage and setting up a basic CI pipeline that runs on every pull request. From there, add automated deployment to a staging environment, then progressively tighten feedback loops and reduce manual gates.

Even if your organisation is not ready to adopt CD fully, demonstrating that you understand the principles and can articulate their value—to your team, your manager, or a future employer—is itself a meaningful career asset. Continuous Delivery has moved from a competitive advantage to an industry expectation. Developers who are fluent in it are better positioned to do impactful work, earn recognition, and take on increasing levels of responsibility.

If you are working in enterprise IT and want to move into cloud-focused roles, the following three steps can help you take advantage of the current demand without waiting for a formal job posting to appear.

Step 1: Invest Serious Time in Learning

Cloud technology moves quickly, and employers can tell the difference between someone who has surface-level awareness and someone who has put in real hours. To build genuine depth:

• Attend local meetups. Most major cities have active AWS, Azure, Google Cloud, and cloud security meetup groups. Attending regularly exposes you to practitioners, real-world use cases, and the people hiring for cloud roles.
• Pursue relevant certifications. Vendor certifications from AWS, Microsoft (Azure), and Google Cloud are widely recognized and actively sought by employers. Most exam prep courses are available on-demand, so you can study on your own schedule without taking time away from your current job.
• Build hands-on experience. Free and low-cost cloud tiers let you spin up real infrastructure. Working through labs and personal projects is far more convincing on a résumé than certification alone.

The investment is primarily one of personal time, which is a realistic ask for anyone who is serious about a career change.

Step 2: Make Your Skills Visible Inside Your Organization

Skills that no one knows about will not advance your career. Once you have built a solid foundation, make sure the right people in your company are aware of it. There are several low-pressure ways to do this:

• Run internal workshops or lunch-and-learns. Offering to explain cloud concepts to colleagues positions you as a subject-matter expert and creates goodwill. Even a short session on cloud cost management or container basics can have a lasting impression on managers who attend.
• Write and publish. Contribute to internal wikis, newsletters, or engineering blogs. If your organization does not have these, look for external publications, community forums, or platforms like Medium or Dev.to. A published article with your name on it is a durable signal of expertise.
• Be helpful, not boastful. The goal is for decision-makers to associate your name with cloud competency. Answer questions in internal Slack channels, review architecture proposals, and offer opinions when cloud topics come up in meetings. Consistency matters more than any single gesture.

Step 3: Target Emerging Cloud Projects, Not Just Open Roles

Job postings are a lagging indicator of where a company is actually heading. By the time a cloud role is formally advertised, a project is often already underway and the preferred candidates may already be identified. Getting ahead of that process is the key to converting your skills into a better role or salary.

• Map out where cloud is heading in your organization. Talk to colleagues in architecture, finance, and DevOps to understand which teams have cloud budget and which projects are on the roadmap. Quarterly business reviews, internal all-hands meetings, and budget announcements often contain this information if you listen carefully.
• Offer to help before you are asked. Reach out to project owners and let them know you have relevant skills. Many cloud initiatives struggle with staffing, and a capable internal resource is often preferable to an expensive external consultant.
• Negotiate from a position of demonstrated value. If you are already contributing to a cloud project when headcount decisions are made, you are in a far stronger negotiating position than an external candidate. Use that moment to discuss a formal title change, pay adjustment, or expanded scope.

The Bigger Picture

The pattern described here is not unique to cloud computing—it applies any time a new technology creates skill gaps faster than the job market can fill them. The combination of deliberate learning, internal visibility, and proactive project involvement is a repeatable strategy for anyone willing to put in the effort.

Cloud adoption is still accelerating across industries. Professionals who build credible cloud skills now, and make those skills visible to decision-makers, are well positioned to move significantly faster than the standard enterprise promotion cycle would otherwise allow.

Originally inspired by a piece published on InfoWorld (2017). The advice has been updated and expanded for a current DevOps engineering audience.