The challenge is that DevOps as a discipline is still maturing. Many people claim expert-level DevOps knowledge but lack the practical experience, contextual insight, and people skills needed to be effective mentors. The following attributes — drawn from practitioners and advisors across the industry — provide a practical framework for evaluating candidates.

Experience Across Both Development and Operations

A qualified DevOps trainer needs grounding in both software development and IT operations — not just one or the other. According to Jai Schniepp, Director of Product for Secure DevOps Platforms at Liberty Mutual Insurance, you want someone who can navigate the natural friction that arises when two historically siloed disciplines are brought together.

A trainer with dual-discipline experience can recommend best practices and appropriate tooling that bridges the developer and operations perspectives, rather than defaulting to a generic, one-size-fits-all approach. Schniepp notes that such a trainer “can maintain focus on the skills and structure needed for the organization,” tailoring guidance to the team’s actual context rather than imposing a rigid framework.

Beyond technical breadth, a strong trainer should also demonstrate organizational aptitude — the ability to prioritize competing concerns, remain flexible as circumstances evolve, and maintain empathy for both the delivery team and the customers they serve.

Emotional Intelligence Is Non-Negotiable

Technical knowledge alone does not make a great DevOps trainer. Justin Rodenbostel, Vice President of Delivery at digital transformation advisory firm SPR, argues that emotional intelligence (EQ) may be the single most important attribute to evaluate.

DevOps adoption is frequently a radical organizational change. It disrupts established workflows, reshuffles responsibilities, and challenges long-held assumptions about how development and operations teams interact. Where radical change occurs, human resistance follows. A trainer who lacks empathy and self-awareness will struggle to bring skeptical team members along — no matter how technically proficient they are.

High EQ enables a trainer to read the room, adjust communication style for different audiences, de-escalate tension, and build the psychological safety that teams need to experiment and learn from failure — all of which are prerequisites for a healthy DevOps culture.

Soft Skills and Change Management Capability

Alan Zucker, Founding Principal of Project Management Essentials, emphasizes that at the start of a DevOps initiative, soft skills training is at least as important as technical instruction. DevOps is fundamentally a mindset — a set of practices and principles that enable organizations to deliver value more rapidly and reliably. It draws on systems thinking to challenge assumptions about end-to-end product value streams and how teams organize their work.

This means effective DevOps trainers must be skilled in:

• Communication: Translating complex technical concepts for non-technical stakeholders, and vice versa.
• Collaboration and facilitation: Running workshops, retrospectives, and cross-functional planning sessions that produce real alignment.
• Negotiation and conflict resolution: Helping teams navigate disagreements about tooling, processes, and ownership boundaries.
• Motivating toward a shared goal: Articulating a compelling vision for what DevOps will deliver and rallying people behind it.

Sara Faatz, Senior Manager of Developer Relations at Progress, frames this succinctly: the trainer must be trustworthy and respected — in effect, a leader. Getting the “people part” of DevOps right — building trust, earning respect, and communicating effectively — is paramount to success.

Technical Depth and Hands-On Practical Experience

While soft skills set the foundation for adoption, technical credibility is what earns a trainer lasting respect from engineering teams. Josh Komoroske, Senior DevOps Engineer at StackRox (a container security software firm), highlights two specific technical requirements:

• Product and toolchain familiarity: The trainer should know the specific tools the team will use — CI/CD pipelines, container orchestration, infrastructure-as-code frameworks, monitoring stacks, and so on — not just DevOps theory in the abstract.
• Real-world deployment experience: Book knowledge of Kubernetes or Terraform is not sufficient. A trainer who has actually deployed, operated, and troubleshot these systems in production environments brings irreplaceable context. They can help teams find practical, creative solutions to real-world DevOps challenges rather than prescribing idealized workflows that don’t survive contact with production systems.

Komoroske also notes that familiarity with the underlying programming language landscape used by the team is a meaningful advantage — it allows the trainer to contextualize DevOps tooling recommendations within the team’s existing technical reality.

Genuine Enthusiasm for Teaching

One attribute that is easy to overlook but difficult to fake is a genuine passion for helping others learn. A trainer who derives satisfaction from watching team members grow will invest more deeply in individuals, adapt their approach when someone isn’t keeping pace, and remain patient when concepts need to be revisited. This quality often separates trainers who produce durable capability from those who deliver a one-time engagement and move on.

Adaptability and the Ability to Hold Multiple Perspectives

No two organizations adopt DevOps in exactly the same way. Team structures, regulatory environments, legacy technology constraints, and cultural starting points all vary. An effective DevOps trainer must be able to view problems from multiple angles and adapt their recommendations accordingly.

As Komoroske puts it: “Different people value different things, and being able to understand this leads to better adoption.” A trainer who insists on a single canonical approach regardless of context will find adoption rates lag — while one who can flex their approach to meet teams where they are will drive faster and more durable change.

Evaluating These Attributes During the Selection Process

Assessing technical skills through structured interviews, code reviews, or tool-specific demonstrations is relatively straightforward. Assessing soft skills and emotional intelligence is harder, but not impossible.

For internal candidates, Faatz recommends 360-degree reviews — gathering structured feedback from the candidate’s peers to understand whether they have already earned trust and respect within the organization. For external trainers, the interview process itself is revealing: observe how they explain complex concepts, how they respond to pushback or skeptical questions, and whether they adapt their communication style to the audience in the room.

Requesting references from previous training engagements and asking referees specifically about the trainer’s ability to manage resistance and communicate with non-technical stakeholders can surface insights that a standard interview will miss.

Summary: A Checklist for Evaluating DevOps Trainers

• Background in both software development and IT operations
• High emotional intelligence and demonstrated empathy
• Strong communication, facilitation, and conflict-resolution skills
• Hands-on experience with the tools and platforms your team uses
• Familiarity with real-world deployment and operations challenges
• Genuine enthusiasm for teaching and mentoring
• Ability to tailor their approach to your organization’s specific context
• Trustworthiness and credibility as a leader — not just a subject matter expert

A trainer who combines deep technical experience with strong interpersonal skills and genuine adaptability is rare — but investing the time to find that person will pay dividends throughout your DevOps transformation and beyond.

This article draws on practitioner perspectives originally published by InformationWeek.

DevOps isn’t a new adventure for us at PTC. We first started our DevOps journey about four years ago with a team that was primarily focused on release management, software deployment, and automation of these two functions across the development team.

We started early, but I wouldn’t say we crossed the finish line with that first iteration. We incorporated some automation tools, software packaging, and release management, but it was more ad hoc and informal. We knew we needed to refocus our DevOps efforts, and have spent the last 18 months doing just that. 

Lessons from the second leg of our DevOps journey

Our focus with DevOps 2.0 has centered around CI/CD and continuous testing across all the scrum teams.

Our focus with DevOps 2.0 has centered around CI/CD and continuous testing across all the scrum teams in IT. The DevOps function has been really impactful in bringing a consistent approach across product lines and multiple technical platforms.

Today we’re a little more than halfway done with our DevOps 2.0 journey. We still have a lot of work to do, particularly around bringing all teams up to the same level of maturity to ensure a consistent level of performance, expectation, and velocity. We’ve made a lot of progress in our journey and have learned some valuable lessons along the way.

[ Get our free eBooks: DevOps Hiring: The Ultimate Guide and IT job searching in 2019: A practical guide. ]

Lesson 1: Prioritize change management

When we first dove into DevOps several years ago we underestimated the importance of change management across the various teams. Instead, our focus was misplaced on best practices, tools, and new processes.Focus on helping individuals and teams understand how their roles have changed, and what that means for them moving forward.

If I could go back and do things differently, I’d place a stronger focus on change management to help each of the teams and individuals understand how their role has changed. This would also include finding teams that are true champions and enlisting them to show other teams the path to success. It would mean identifying some early successes, finding out why some teams were more successful in adopting best practices, and promoting those ideas more so we could demonstrate the benefits across all the teams, ensuring that they’re all at the same level. 

This time around, we focused on exactly that – helping individuals and teams understand how their roles have changed, and what that means for them moving forward. 

Lesson 2: Do agile and DevOps concurrently

You can’t do agile before DevOps, and you can’t do DevOps before agile; they need to be done at the same time. Here’s why: Agile practices around development, like having product owners and scrum teams, will come to a screeching halt if CI/CD is not deployed fully.During our original foray into DevOps, we tried to focus on operations first, without the upstream development using agile.

You need to take both of those practices and move them along concurrently. They’re so tightly integrated that you can’t really separate them. During our original foray into DevOps, we tried to focus on operations first, without the upstream development using agile. That’s why there was a mismatch in collaboration with these teams, and collaboration is very, very key in order for DevOps to be successful.

Lesson 3: Consider geolocations

We discovered that teams that were farther away from the center of gravity — Boston, in our case — should have more focus on training and adoption. Otherwise, messaging gets diluted, which we found was a factor that impacted adoption. Team members who were farther away were less-immersed in the day-to-day processes than those who were geographically closer.

Lesson 4: Prioritize training

If you have multiple teams working on a single project and there’s a different level of maturity across this teams, the weakest team drags down the entire project. We learned this lesson the hard way. 

It wasn’t easy for us to figure out why velocity was slow, but we ultimately discovered that it was due in part to the differing levels of maturity across those teams. This time around, it was essential for us to make sure all teams were up to speed first. That’s where training came into play.

We had three focuses for training: First, we rolled agile into our journey and got it adopted across all teams via a consistent practice to ensure everyone was up to speed. Second, we ensured everyone was on the same page with CI/CD from both a best-practices and a tools standpoint. Finally, we looked at how their work changed with the adoption of these new practices. This highlighted how they did things before, and how they’d do things differently. 

What’s next 

When we relaunched our focus on DevOps, we did so deliberately, announcing the roles that were changing that would help us focus on this new way of doing things. There was a lot of communication across all teams, and we even went on a roadshow to talk about how we’d be deploying this and the benefits of relaunching it in a different way. 

I was also very transparent with my direct reports about areas in which we needed to improve. This was part of the culture I’ve developed at PTC – never minimizing or maximizing our successes or failures. We don’t need to mince words or soften things up: Let’s just admit it, learn from it, and move on. 

For others on this journey, it’s important to understand that every organization is different. You need to look at yours and your culture to determine how best to adopt DevOps to your organization. The same model might not work everywhere. 

Every IT organization needs to undertake this journey, however. It’s just too impactful not to. At PTC, for example, our speed of deployment has increased considerably. We used to do quarterly releases for some of our major applications and now we have a major release every month. 

Business demands are increasing and IT teams need to be more flexible while maintaining error-free control. The only way to do this is with a very deliberate process – the adoption of good DevOps practices and good agile practices. 

The following seven incidents — sourced from a compilation by UpGuard and originally reported by CSO Online — illustrate how well-intentioned automation can fail spectacularly when testing, visibility, and configuration discipline are absent. Each case carries concrete lessons for anyone responsible for deploying and operating automated systems.

1. Healthcare.gov (2013): Insufficient Testing Before a High-Stakes Launch

When the U.S. government launched the Affordable Care Act’s web enrollment portal, Healthcare.gov, in October 2013, the stakes were enormous: millions of citizens needed to access health insurance options through this single platform. The site crashed within two hours of launch.

The root cause was not a single dramatic error but an accumulation of over 100 known defects in the account-creation module, internally called Account Lite. This component was central to the user experience — without it, no one could register and access coverage options. Despite the known defect count, contractors moved the code forward to production without resolving the underlying issues.

The failures that should have been addressed during integration and testing were instead fixed after the public outage — under intense political scrutiny. Beyond the technical damage, the crash became a political liability, cited by critics as evidence that the Affordable Care Act itself was unworkable.

Key Lesson

Automation cannot substitute for integration testing. When known defects are deferred to post-launch remediation on a high-visibility system, the cost — technical, financial, and reputational — is always higher than fixing them before release.

2. Dropbox (2014): A Subtle Script Bug Cascades Across Production Servers

In January 2014, Dropbox’s team initiated a planned product upgrade — a routine operation. A subtle bug in the deployment script caused it to apply updates to a small number of active machines in an uncontrolled manner, which then cascaded across thousands of production servers, taking the service offline for approximately three hours.

The incident highlighted both a failure mode and a success story. Dropbox’s emergency response procedures were well-designed and effective for most services, restoring them within the three-hour window. However, for larger databases the recovery took several days — demonstrating that even good incident response has limits when the underlying data volume is large.

Key Lesson

Deployment scripts that target production servers must be tested against staging environments that closely mirror production at scale. Canary deployments or staged rollouts can contain the blast radius when a script behaves unexpectedly. Recovery procedures are important, but they are not a substitute for preventing the failure in the first place.

3. Amazon DynamoDB (2015): A Single Network Disruption Triggers a Cascade

In September 2015, a network disruption affected a portion of Amazon DynamoDB’s storage servers. When the disruption occurred, a large number of storage servers simultaneously attempted to retrieve their membership data. The retrieval requests exceeded allowed timeouts, causing those servers to remove themselves from the request pool — a self-protective mechanism that became part of the problem.

As the unavailable servers began retrying their requests, the retry storm compounded the original timeout issue into a broader network outage. The feedback loop — disruption causes timeouts, timeouts cause self-removal, self-removal causes retries, retries worsen congestion — took AWS services down for approximately five hours.

This is a textbook example of a cascading failure: an automated retry mechanism, designed to improve resilience, instead amplified a small disruption into a widespread outage.

Key Lesson

Retry logic, circuit breakers, and self-healing mechanisms must be designed and chaos-tested together. Automated recovery behaviors that are individually sensible can interact catastrophically under real failure conditions. Exponential backoff with jitter and circuit breaker patterns exist precisely to prevent retry storms like this one.

4. Opsmatic: A Forgotten Chef Recipe Runs Against Production

Opsmatic’s incident is a cautionary tale about the danger of forgotten automation artifacts. During early experimentation with AWS, the team created a Chef recipe called remove_default_users. Long after the experimental phase ended, that recipe was still running — this time against production servers, not test environments. The staff responsible for maintaining those servers were unaware it was executing.

The result was a complete operational shutdown caused not by a new mistake but by the slow accumulation of unchecked assumptions: that old test configurations had been cleaned up, that automation inventory was accurate, that production was isolated from experimental tooling. None of those assumptions had been validated.

Key Lesson

Automation artifacts — scripts, recipes, runbooks, cron jobs — must be tracked with the same rigor as application code. Regular audits of what is running against which environments, and strict separation between test and production automation pipelines, are not optional practices. “If it ain’t broke, don’t fix it” is a dangerous heuristic when applied to automation you haven’t verified is scoped correctly.

5. Knight Capital (2012): A Misconfigured Server Loses $465 Million in 45 Minutes

Knight Capital’s 2012 trading loss is one of the most dramatic examples of how configuration drift combined with manual deployment can produce catastrophic results. The firm operated algorithmic trading systems distributed across server banks, with the requirement that all servers in a cluster run identical configurations.

During a manual code deployment, a technician failed to deploy updated code to one of the eight servers in the cluster. That server was left running decommissioned code — code that had not been intended for production use. Because the deployment process did not include automated verification that all servers were in the expected state, the discrepancy was not detected.

When trading began, seven servers executed the intended logic. The eighth executed the old, decommissioned logic. The result was an automated feedback loop of unintended stock orders, reportedly costing Knight Capital approximately $465 million in trading losses — or roughly $172,000 per second for 45 minutes. The firm did not survive as an independent company.

Key Lesson

Manual deployment steps in otherwise automated systems are high-risk failure points. Automated post-deployment verification — confirming that every node in a cluster is running the expected version and configuration — is essential. Configuration drift between nodes must be detected immediately, not discovered after it causes an incident.

6. Delta Airlines (2017): A Single Glitch Grounds Operations and Costs $150 Million

Airlines operate some of the most complex real-time logistics systems in the world, with automated tools managing scheduling, crew assignment, gate operations, and passenger systems simultaneously. When those automated systems experience misconfigurations, the failures propagate rapidly through tightly coupled operational dependencies.

In January 2017, Delta reported to investors that a single misconfiguration in its automated operations system triggered a broad outage that disrupted flight operations, delayed aircraft, and cost the airline more than $150 million. The speed with which an automated misconfiguration can spread across an interconnected system is precisely what makes it so damaging.

Key Lesson

In tightly coupled operational systems, configuration changes must be staged, validated, and deployed with rollback capability. The faster automation can propagate a change, the faster it can propagate a mistake. Change management discipline and automated validation gates are critical safeguards.

7. Google Gmail (2014): Automated Configuration Propagates the Wrong Change to Production

Google is a well-documented pioneer of large-scale configuration management automation. In 2014, a bug in Google’s internal automated configuration system caused Gmail to crash for approximately 30 minutes. An incorrect configuration was pushed to live services, causing user requests to be rejected and downstream services to generate errors in response.

While 30 minutes may seem brief, at Gmail’s scale — serving hundreds of millions of users — even a short outage represents a significant disruption. The incident also illustrates an important conceptual distinction that many teams conflate.

Configuration Automation Is Not Configuration Management

Configuration automation ensures that changes are propagated consistently and quickly across all systems. Configuration management is the broader discipline of tracking, validating, auditing, and governing what configurations should be applied and when. Automation without management means that when the wrong configuration is committed, it is deployed everywhere — fast.

Key Lesson

Every automated configuration pipeline needs validation gates before changes reach production. Policy-as-code tools, pre-deployment linting, and staged rollout strategies (with automated rollback on error rate increase) are standard mitigations. Speed of propagation is a risk multiplier, not just a benefit.

Common Threads Across All Seven Failures

Reviewing these incidents as a group, several patterns emerge that DevOps and SRE teams can act on directly:

• Lack of pre-production validation: Healthcare.gov and Gmail both pushed known or undetected problems to production that testing should have caught.
• Configuration drift between nodes: Knight Capital’s disaster was driven entirely by one server diverging from the rest of the cluster without detection.
• Forgotten or orphaned automation: Opsmatic’s outage came from automation that outlived its intended scope.
• Cascading retry loops: Amazon’s DynamoDB outage shows that self-healing behaviors must be designed with their interactions in mind, not just their individual intent.
• No staged rollout or blast radius control: Dropbox and Gmail both suffered from changes propagating too broadly, too fast.
• Manual steps in automated pipelines: Knight Capital is the clearest example of how one manual deployment step becomes the weakest link in an otherwise automated chain.
• Treating automation as management: Across nearly all of these cases, teams assumed automation was handling correctness, not just execution speed.

Conclusion

Automation failures are rarely caused by automation itself. They are caused by absent testing, unaudited artifacts, configuration drift, missing validation gates, and the human tendency to assume that a system is operating correctly because no one has reported that it isn’t. These seven cases collectively span healthcare, e-commerce, financial services, cloud storage, and aviation — demonstrating that no industry is exempt.

The goal of DevOps automation is not simply to make changes faster. It is to make correct, validated, observable changes faster. Speed without governance is how $465 million disappears in 45 minutes.

Original source compilation: UpGuard, as reported by CSO Online.

Why Tech Jobs Dominate Salary Rankings

According to an Indeed job market report cited by TechRepublic, tech roles dominate the top 10 highest-paying jobs in America when measured by a combination of average base salary, number of open job postings, and year-over-year growth. Most roles on the list carry average base salaries exceeding $100,000 per year.

The underlying driver is a widening gap between supply and demand. As organisations across every sector accelerate digital transformation, demand for workers with deep technical skills is growing significantly faster than the talent pipeline can supply them. The result is sustained salary growth and a high volume of unfilled positions.

Seven of the top 10 roles are software engineers or developers; the remaining three are technical managers or consultants. Below is a detailed breakdown of each role.

The Top 10 Highest-Paying Tech Jobs

1. Full Stack Developer — Average Base Salary: $110,770

Full stack developers hold the top spot on the list. They are responsible for both front-end (user interface) and back-end (server, database, API) work, making them highly versatile and valuable assets to any engineering team. At the time of the report, Indeed listed 641 open positions, representing a 122% year-over-year increase in job postings since 2013 — the highest growth rate on the list.

2. Data Scientist — Average Base Salary: $129,938

Data scientists command the highest average base salary on this list at $129,938. They apply statistical modelling, machine learning, and data analysis to extract actionable insights from large datasets. Job postings grew 108% since 2013, with 360 open roles recorded at the time of the report. The combination of high salary and strong growth makes this one of the most competitive tech disciplines to enter.

3. DevOps Engineer — Average Base Salary: $123,165

DevOps engineers sit at the intersection of software development and IT operations. They design, build, and maintain the automated pipelines, infrastructure, and tooling that allow development teams to ship software faster and more reliably. Indeed listed 731 open DevOps engineer positions — the highest raw count on the list — with an average base salary of $123,165 and 106% role growth since 2013.

Core skills typically expected of a DevOps engineer include CI/CD pipeline management, infrastructure-as-code (tools such as Terraform or Ansible), containerisation (Docker, Kubernetes), cloud platforms (AWS, Azure, GCP), and scripting languages such as Python or Bash.

4. Salesforce Administrator — Average Base Salary: $89,702

Salesforce administrators configure, manage, and optimise Salesforce CRM environments for their organisations. With 80 open roles and 103% growth since 2013, the Salesforce ecosystem consistently produces strong demand for certified professionals, even at the administrator level — which requires less coding than a developer role.

5. IT Engineer — Average Base Salary: $85,563

IT engineers oversee the design, implementation, and maintenance of an organisation’s technology infrastructure. With 41 open positions and 89% role growth recorded, this is a broader category that can encompass network engineering, systems administration, and infrastructure management depending on the employer.

6. Salesforce Developer — Average Base Salary: $108,089

Unlike administrators, Salesforce developers write custom code using Apex and Visualforce (and more recently Lightning Web Components) to extend the platform’s capabilities. With 230 open roles and 83% growth since 2013, developer demand is driven by the platform’s continuous expansion into AI, analytics, and integrations with third-party services.

7. Quality Engineer — Average Base Salary: $71,111

Quality engineers — sometimes referred to as QA engineers or reliability engineers depending on the organisation — oversee the development, operation, and maintenance of IT systems and enterprise architectures to ensure they meet defined quality standards. With 311 open positions and 83% growth since 2013, the role is well-established and in steady demand.

8. Digital Project Manager — Average Base Salary: $73,169

Digital project managers bridge the gap between technical teams and business stakeholders, planning and delivering technology projects on time and within budget. With 58 open jobs and 75% role growth at the time of the report, this is one of the few non-engineering roles on the list, making it accessible to professionals with strong organisational and communication skills alongside a working understanding of technology.

9. Cloud Engineer — Average Base Salary: $118,878

Cloud engineers design, build, and manage cloud infrastructure across platforms such as AWS, Microsoft Azure, and Google Cloud Platform. With 217 open roles and 67% growth since 2013, cloud engineering offers a strong salary trajectory. As cloud adoption has only accelerated since this report was published, demand for this role has continued to intensify well beyond what the 2017 data captured.

10. Management Consulting Analytics Manager — Average Base Salary: $90,994

Rounding out the top 10, management consulting analytics managers apply data analytics frameworks to advise organisations on strategic decisions. With 47 open jobs and 66% growth since 2013, this role sits at the boundary of business consulting and data science, typically requiring both analytical capability and client-facing communication skills.

Summary Table

Key Takeaways for Tech Professionals

• DevOps and cloud engineering offer the best combination of high salary, high open-role count, and strong growth — making them particularly attractive specialisations for engineers considering a career pivot.
• Data science commands the highest average base salary on the list, but typically requires advanced skills in statistics, machine learning, and programming.
• Salesforce roles (both administrator and developer) provide a lower barrier to entry relative to general software engineering, with clear certification pathways and strong market demand.
• Salary figures are from 2017 and should be treated as a directional baseline. Current market rates are higher across all these categories. Consult up-to-date sources such as the US Bureau of Labor Statistics, Glassdoor, or Levels.fyi for current compensation data.

Original data source: Indeed job market report, as reported by TechRepublic (2017).

With the advancement of technology, more and more jobs are being created. Some as to this day are still growing and do not have a precise definition as to what that job really do, because it may differ from companies.

Here are said to be the tech jobs that will boom this 2017. Just a heads up, the list is based on some researchs, while the definition of the job descriptions is taken and based on various websites, but most of them are from Wikipedia.

DevOps Engineer
For this job’s description, it is hard to say, because in reality, this type of job is still evolving and each company has its own definition of putting it. However, the best answer found on the web so far is this:

“In its purest form, a DevOps Engineer bridges the gap between developers and operations. DevOps Engineers usually will automate the tasks of building

an infrastructure and the process of shipping software from development into production in an automated manner.”

Data Scientist
The Data Scientist is reported to be the number one on-demand tech job in the market for two years in a row. The job is designed for a person who analyzes and interprets complex digital data such as the usage statistics of a website in order to assist a business or company on its decision-making.

Technical Program Manager
These people are in charge of handling all aspects of the technical project for their organization. Part of their job is to be responsive for initiating programs, following their progress, and serving as support provider in case issues arise.

Analyst Manager
Another tech job that is said to boom this year is Analyst Manager. These people are business professionals who use their technical skills, industry understanding, and knowledge of customers to prepare and present information for decision-making.

Data Engineer
The next tech job we have is Data Engineers. Basically, these people are builders and managers of the information or “big data” infrastructure.

UI Designer (aka User Interface Designer)
For this job, there is also has no definite job description, because most people confuses it with a UX (User Experience) Designer. So it will be defined with an example instead:

“As a UI designer, you’ll work very closely with UX designers, whose job typically consists of talking with customers to determine requirements, building user profiles and creating user stories to show how a user will work their way through your application.

From this point, it’s up to you to build a clean and functional design based on the requirements they’ve gathered.”

Scrum Master
Another promising tech job, but does not have the precise job description because it varies with companies. However, there was one person who described it as a “servant-leader” position. And for the type of tasks this job holds are some of the following: keep Scrum process running, ensures a proper power balance between PO, Team, and Management, protects the Team, and moderates the Team.

Software Engineer
Next on the list is Software Engineer. Essentially, this is a position who applies principles of software engineering to the design, development, maintenance, testing, and evaluation of the software and systems that make computers or anything the contains software work.

Data Architect
Another tech job is Data Architect. This is someone who creates and manages large and expensive electronic databases to store and organize vast amounts of data.

Customer Success Manager
Lastly is the Customer Success Manager. These people act as the main point of contact and as a trusted advisor for the customer. So basically, they are the ones ultimately responsible and accountable for the customer’s success.