How many certifications you have and where you live will likely drive what you make as a cloud engineer. Catch the latest cloud salary and hiring trends.

COVID-19 has caused a spike in demand for lucrative cloud-related roles, which could offer engineers an opportunity to take the next step in their careers. However, the adoption of remote working strategies could complicate how professionals are compensated across the IT industry going forward.

The average annual salary for a cloud engineer is just under $129,000, according to ZipRecruiter, an employment marketplace. But this number will vary depending on a few factors, such as where IT pros live and the industries they work in. However, recent industry salary reports indicate that many of the variables that determine cloud computing salaries are in the job candidates’ hands.

For example, a job candidate can boost salary potential by earning relevant certifications or relocating to an emerging tech hub — like Columbus, Ohio or Denver.

Demand for cloud engineers and architects has gone up since the outbreak began, according to Scott Engler, an analyst at Gartner. But the rise of remote work has increased the supply of available talent as well.

“The supply-demand equation just changed,” Engler said. “It allows for more wage arbitrage.”

Remote work also changes the compensation equation around cost of living. A cloud professional now working remotely with a coastal salary may want to relocate to a city with lower living expenses.

Two of the biggest factors that determine your cloud computing salary are how many certifications you have and where you live. Learn how COVID-19 and remote work have altered this equation.

Raise your skills with certifications

IT pros often get certified in specific technologies to improve their skills, but industry certifications are also a popular and affordable way to earn salary bumps.

According to Global Knowledge’s 2019 IT Skills and Salary Report, 85% of IT professionals worldwide — and 81% in North America — have at least one certification. Global Knowledge, a provider of IT skills and training services, examined multiple industries and locations and found that certified professionals earn more than their non-certified counterparts. With each additional certification there is also a small bump in salary. In North America, a certified IT professional earns 7% more than someone who is not certified — the average salary jumps around 2% for each additional certification earned.

Of the 10 certifications that attract the highest salaries in North America, four are cloud related. The Google Cloud Platform Cloud Architect certification is the highest on that list, with certificate earners averaging an annual salary of $152,129. Three different AWS certifications — AWS Certified Solutions Architect – Associate, AWS Certified SysOps Administrator – Associate or AWS Certified Developer – Associate — can translate to salaries around $130,000 a year.

In addition, cross-certifying with a breadth of certifications is a strong way to improve an IT pro’s cloud computing salary, according to Global Knowledge. Cross-certifying with AWS is a particularly attractive option in North America. For example, pairing an AWS certification with one from VMware, Cisco or CompTIA can increase a candidate’s income by more than $10,000.

While in-person certification exams are unavailable at time of publication due the pandemic, each major cloud platform offers certification exams with online proctoring.

The benefits of earning multiple certifications
Relocation is an enticing option, for now
Relocation has long been a way to increase yearly earnings, but that doesn’t mean you should head straight to the San Francisco Bay area. When looking to relocate, cloud professionals need to factor in where salaries are rising, as well as the cost of living in those places.

Cloud professionals just starting out can also pursue opportunities in what Dice, an employment marketplace, calls “emerging tech hubs” in its 2020 Tech Salary Report. In these emerging tech hubs, IT and tech salaries are rising, and cost of living is still relatively low. The highest increases in IT salary have occurred in Columbus, Ohio (14.2%), St. Louis (13.6%), Atlanta (9.5%) and Denver (6.5%).

The 2020 State of Salaries report from Hired, an employment marketplace focused on technology roles, evaluated how far your tech salary would go in certain cities. The report adjusted the average tech salary in major U.S. cities for Bay area living expenses. The report found that adjusted salaries in Austin, Texas and Denver became $224,000 and $202,000, respectively. This explains how, when evaluating whether or not to relocate, candidates should look for how far their money will go, not necessarily how much they could earn.

Remote work brings relocation wrinkle
The rise of remote work has raised a few issues around compensation, especially in terms of cost of living. According to Hired’s report, 53% of tech professionals said a permanent remote working situation would make them likely to move to a city with lower living expenses. If IT pros are working remotely for the foreseeable future, it makes sense for many to move to a more affordable location. However, employers may not want to pay Silicon Valley salaries for employees working from home in Montana.

For example, Facebook said it could shift half of its workforce into remote work within five years, but it would also adjust salaries for cost of living. So, if a Facebook employee moves out of Silicon Valley to work remotely in a more affordable area, the employee’s salary could be reduced.

Unsurprisingly, tech professionals are not on board with altering salaries based on remote work situations. Most tech talent believe remote and in-office employees should be paid the same and would not take a pay cut to work from home on a permanent basis, according to the Hired report.

Remote work has begun to change the way companies seek and compensate tech talent, Engler said. Organizations are now looking to identify pockets of talent around the world and have found them in places like Warsaw, Poland; Chennai, India; Melbourne, Australia; St. Petersburg, Russia; and Mexico City. Remote work offers more opportunity for cloud professionals around the world, but that also means more competition for employment opportunities.

“You’re now going to be competing as a job seeker with someone from Warsaw with some companies,” Engler said.

The tech industry might see wage arbitration on a macro scale over the next year as these conflicts come to a head, Engler said. Employers and job seekers will have to hash out exactly how remote and hybrid work strategies change compensation models over the next year.

1. Post Graduate Certification in Big Data Analytics — E&ICT, IIT Guwahati

This programme is designed for graduates who want to build a career in Big Data Analytics, Data Science, or Machine Learning. It spans 11 subject areas including Statistics for Analytics, Data Analytics with Excel and SQL, Python, Java, Linux, Big Data and Hadoop, Apache Spark, MongoDB, Business Intelligence and Data Mining, and Tableau.

• Duration: 9 months
• Price: ₹2,25,000
• Eligibility: Graduates with a minimum of 50% marks in their undergraduate degree
• Certificate: Joint certificate from E&ICT IIT Guwahati and IBM upon completion

2. Machine Learning — Coursera

One of the most recognised introductory machine learning courses available online, this Coursera offering covers logistic regression, artificial neural networks, and a range of ML algorithms. The course balances theoretical foundations with practical implementation exercises, making it suitable for learners who want to apply ML techniques to real problems quickly.

• URL: coursera.org/learn/machine-learning
• Duration: Approximately 64 hours
• Price: ₹4,508 (pricing may vary; audit option may be free)
• Eligibility: Open to all learners
• Certificate: Electronic certificate, shareable on LinkedIn

3. Data Science Architect Master’s Course — Intellipaat

This master’s-level programme covers the full breadth of Data Science tooling: R, Hadoop, Apache Spark, Scala, Deep Learning, Tableau, SAS, SQL, and MongoDB. The curriculum includes 10 core courses, 53 industry projects, and one Capstone project. Learners also receive five IBM-co-created self-paced courses covering Deep Learning with TensorFlow, chatbot development with Watson Assistant, R for Data Science, Spark MLlib, and Python for Data Science.

• URL: intellipaat.com — Data Science Architect
• Duration: 232 hours of online instructor-led training
• Price: ₹62,000
• Eligibility: No prerequisites
• Certificate: IBM certificate upon completion

4. Deep Learning Explained — edX

This edX course provides a practical introduction to deep learning. Topics include multi-class classification using logistic regression, handwritten digit recognition with deep neural networks and convolutional networks, time-series forecasting with recurrent networks, and text processing using LSTM units. It is a good entry point for those with a data science background who want to move into neural network architectures.

• URL: edx.org — Deep Learning Explained
• Duration: 6 weeks
• Price: Free to audit; verified certificate costs $99
• Eligibility: Basic programming skills and working knowledge of data science recommended

5. Cloud and DevOps Architect Master’s Course — Intellipaat

This programme is directly relevant to DevOps engineers and cloud practitioners. It covers AWS, Azure, DevOps toolchains, Python, Java, Splunk, and Linux across 11 courses, 26 industry projects, and a Capstone project. Learners also receive official Microsoft course material on integrating on-premises identity infrastructure with Azure and implementing security in Azure development solutions.

• URL: intellipaat.com — Cloud & DevOps Architect
• Duration: 147 hours of online instructor-led training
• Price: ₹56,900
• Eligibility: No prerequisites
• Certificate: Joint IBM and Microsoft certificate upon completion

6. Mastering Python — Networking and Security — Udemy

Python is the de-facto scripting language for network automation and security tooling. This Udemy course focuses specifically on using Python for network scripting and security tasks. It covers script creation, threading, exception handling, and leveraging Python networking libraries to build practical automation scripts.

• URL: udemy.com — Mastering Python: Networking and Security
• Duration: Approximately 5 hours of on-demand video
• Price: ₹360 (Udemy prices fluctuate; check current listing)
• Eligibility: Basic programming awareness helpful but not strictly required

7. Artificial Intelligence Master’s Course — Intellipaat

This AI-focused master’s programme covers Data Science, Python, R, Statistics, Natural Language Processing, SAS, Excel-based analytics, and Tableau. It is designed for learners who want a broad AI and ML skill set without a strict programming prerequisite, though basic programming knowledge will help you progress faster.

• URL: intellipaat.com — Artificial Intelligence Master’s Course
• Duration: 166 hours of online instructor-led training
• Price: ₹46,000
• Eligibility: No prerequisites; basic programming knowledge is an advantage
• Certificate: IBM certificate upon completion

8. Web Development with Django and AngularJS — Pluralsight

This Pluralsight course teaches full-stack web development by combining Django on the back end with AngularJS on the front end. Students build a complete web application, learn to connect front-end JavaScript to back-end Python using REST and AJAX, and finish with a working, interactive application they can extend independently.

• URL: pluralsight.com — Django and AngularJS Web Development
• Duration: 3 hours 42 minutes
• Price: Included in Pluralsight subscription (approximately $19/month at time of writing; check current pricing)
• Eligibility: Open to all learners

9. Business Intelligence Architect Master’s Course — Intellipaat

For data professionals who work closer to the business layer, this BI-focused programme covers Informatica, Tableau, MSBI, Power BI, MS SQL, data warehousing and Erwin modelling, Azure Data Factory, and SQL DBA administration. The programme includes 9 core courses and 32 industry-based projects.

• URL: intellipaat.com — Business Intelligence Master’s Course
• Duration: 150 hours of online instructor-led training
• Price: ₹56,000
• Eligibility: Open to all learners
• Certificate: Microsoft certificate upon completion

10. Intro to Machine Learning with TensorFlow — Udacity

This introductory programme covers foundational machine learning algorithms, starting from data cleaning and supervised learning models and progressing to deep learning and unsupervised techniques. Practical exercises are integrated at each stage, giving learners hands-on experience with TensorFlow alongside conceptual understanding.

• Duration: Approximately 3 months
• Eligibility: Basic Python and statistics knowledge recommended

Choosing the Right Course for Your Role

The courses above span a wide range of investment levels — from free audits to multi-month master’s programmes. Before enrolling, consider the following:

• DevOps / Cloud engineers will get the most immediate value from the Cloud and DevOps Architect Master’s Course or the Python Networking and Security course.
• Data engineers and analysts should look at the Big Data Analytics certification or the Data Science Architect programme.
• Machine learning practitioners can start with the Coursera ML course or the TensorFlow intro and graduate to the Deep Learning Explained or AI Master’s course.
• Full-stack developers moving into data-driven products will benefit from the Django and AngularJS course alongside a BI or SQL foundation.

Prices and course structures change regularly. Always verify the current syllabus, pricing, and certification status directly on the provider’s website before enrolling.

Why Disruption Is a Good Time to Upskill

Workplace disruption rarely feels like a career opportunity, but for DevOps professionals it can be exactly that. When teams shift to remote work, delivery pipelines are stress-tested, automation gaps become visible, and the value of a well-rounded DevOps engineer becomes undeniable to leadership. Rather than waiting for conditions to normalise, this is the right moment to take honest stock of your skills, identify gaps, and start filling them.

There was already a recognised skills and talent shortage in the DevOps field before 2020. That gap has not closed; if anything, accelerated digital transformation has widened it. Organisations that were slow to adopt DevOps practices have been forced to move faster, which increases demand for engineers who can both implement the tooling and guide the cultural change.

What the Upskilling 2020 Report Found

The DevOps Institute released its Upskilling 2020: Enterprise DevOps Skills Report based on a survey of 1,260 individuals asked to identify the skills they consider critical to DevOps and digital transformation. The central concept in the report is what the Institute calls the DevOps human: a practitioner who balances people skills, process knowledge, and technology expertise rather than excelling in only one dimension.

Jayne Groll, co-founder and CEO of the DevOps Institute, framed the challenge this way:

“While organisations are transforming toward digital operating models, challenges of speed and scale of technological advances, IT budgets, and a huge skill gap are causing a slowdown of this fourth Industrial Revolution.”

— Jayne Groll, DevOps Institute

Groll also noted that upskilling and retraining “must be high on every organisation’s agenda” — and that many organisations are actively encouraging staff to use periods of reduced project velocity to learn.

Demand for DevOps Engineers Remains Strong

Christian Mendiola, a recruitment consultant specialising in cloud computing, DevOps, and site reliability engineering (SRE) for financial services companies, observed that the DevOps job market held up better than most during periods of broader hiring slowdowns:

“Companies still need DevOps engineers in this climate. It is one of the few markets we’ve found that has been less impacted by the decrease in hiring.”

— Christian Mendiola, Huxley (SThree)

Sectors with particularly acute needs include finance, healthcare, and ad tech. Mendiola noted that industry-specific experience is less important to hiring managers than strong technology and communication skills — a point worth remembering if you are considering a sector change.

The candidate profile that stands out most, according to Mendiola, is someone who has helped build a DevOps methodology from scratch or matured one from its early stages. This demonstrates both theoretical understanding and practical execution — and shows that the candidate is more than an individual contributor.

The Technical Skills Employers Are Looking For

Jim Mercer, research director for DevOps at IDC, pointed to a clear rise in demand for specific technical competencies, especially those that support remote, automated, and highly reliable workflows:

“Prior to COVID-19, DevOps professionals were in strong demand. In the midst of the crisis, DevOps professionals are still in strong demand, but the demand for effective remote working and coding, automation, and SRE skills is higher given the ‘new normal.'”

— Jim Mercer, IDC

Mercer recommends prioritising the following technical skill areas:

• Agile software development techniques — understanding iterative delivery and cross-functional collaboration
• Developer-driven testing — shifting quality left so defects are caught early
• Highly automated test environments — reducing manual toil and enabling fast feedback loops
• CI/CD pipelines — designing, maintaining, and improving continuous integration and delivery workflows
• Infrastructure as Code (IaC) — provisioning and managing infrastructure through version-controlled configuration
• Configuration management — tools such as Ansible, Puppet, or Chef
• Monitoring, alerting, and logging — building observability into systems from the start
• Code review and coverage practices — maintaining quality gates in delivery pipelines
• Containers and orchestration — hands-on experience with Docker and Kubernetes in particular
• SRE principles — error budgets, SLOs, SLIs, and reliability engineering mindset

This is a broad list, which is intentional. The most competitive DevOps engineers are not one-trick specialists in a single tool; they understand how these technologies connect across the software delivery lifecycle.

Human Skills Are Not Optional

Technical depth alone will not differentiate you. Eveline Oehrlich, independent research director at the DevOps Institute and co-author of the Upskilling 2020 report, argues that human skills are finally being taken seriously across all DevOps roles and geographies:

“The human skills are finally being recognised as important across all roles and regions. They must be mentioned over and over again.”

— Eveline Oehrlich, DevOps Institute

In the context of DevOps, human skills include:

• Empathy — understanding the pressures faced by developers, operations staff, and business stakeholders
• Communication — translating technical concepts clearly for non-technical audiences and vice versa
• Collaboration — working across organisational silos to remove bottlenecks
• Creativity and problem-solving — finding pragmatic solutions when standard playbooks don’t apply
• Interpersonal skills — building trust with teammates and stakeholders

Oehrlich’s framing of the ideal DevOps professional is worth quoting directly:

“The future of DevOps sits atop a hybrid individual — someone who can work across a variety of functional subject areas, leveraging experts and incorporating key topics of automation and process or framework methodologies to deliver value at speed and quality for the customer.”

— Eveline Oehrlich, DevOps Institute

She adds that the highest-demand, highest-paid DevOps professionals are collaborative, flexible, creative, results-oriented, and generous with their knowledge: “A person who only wants to show how smart they are is not going to be successful in DevOps, but a person who is smart and shares his or her smarts is great.”

Mendiola echoes this on the recruitment side: DevOps professionals cannot rely on a business analyst to act as a communication bridge. They need to speak directly and clearly to both technical colleagues and non-technical stakeholders.

How to Stand Out: The Four E’s and Four Strengths

What, concretely, distinguishes top DevOps candidates from the crowd? Oehrlich points to the four E’s:

1. Experience — a real track record of DevOps work across meaningful projects
2. Expertise — deep, demonstrable skill in at least one or more core areas
3. Exploration — a habit of learning, experimenting, and staying current with evolving practices
4. Execution — the ability to ship, not just plan or advise

IDC’s Mercer offers a complementary list of four strengths that hiring managers value most:

1. Proven experience driving DevOps adoption — not just participating in it, but actively pushing it forward
2. Strong people skills — being a good teammate who raises the capability of those around you, not just yourself
3. Cross-disciplinary leadership — the ability to partner across teams and functions to produce high-impact results
4. Analytical and creative problem-solving — generating practical solutions to novel or ambiguous problems

Specialise, Don’t Just Generalise

One final and important piece of advice from Mendiola: avoid the trap of trying to know a little about everything while excelling at nothing.

“As in any profession, the key to standing out is to specialise.”

— Christian Mendiola, Huxley

This does not mean ignoring breadth — a narrow specialist who cannot collaborate across boundaries will hit a ceiling quickly. It means building genuine depth in one or two areas (say, Kubernetes operations and observability, or CI/CD pipeline design and security automation) while maintaining enough breadth to communicate credibly with adjacent roles.

Practical Next Steps for DevOps Upskilling

If you are ready to act on this guidance, here is a starting framework:

• Audit your current skills against the technical list above. Identify two or three genuine gaps.
• Choose a specialisation direction — for example, platform engineering, SRE, security automation, or observability.
• Build hands-on projects. Lab environments, open-source contributions, and internal tooling improvements all count.
• Pursue recognised certifications where they add signal — for example, the DevOps Institute’s DOFD or DASA DevOps certifications, or cloud-provider DevOps-focused credentials.
• Practice communication deliberately. Write internal documentation, give brown-bag presentations, or contribute to technical blogs.
• Seek out DevOps-immature environments. Building a CI/CD pipeline where none existed is far more impressive on a résumé than maintaining one someone else built.

The DevOps talent gap is real and persistent. Engineers who invest in a balanced combination of technical depth, process knowledge, and human skills will find that the market rewards that investment consistently.

That vision sounds compelling on paper, but it often triggers anxiety in one corner of the organisation: the IT service desk. Service professionals worry about being left behind, losing influence, or becoming irrelevant as DevOps teams take the wheel. Those concerns are understandable — but they are largely unfounded. DevOps and IT Service Management (ITSM) are not rivals. Handled correctly, they are complementary disciplines that reinforce each other.

Why Service Teams Feel Threatened by DevOps

When DevOps initiatives are announced, service desk staff frequently ask the same question: What happens to us? The concern usually comes from a misunderstanding of what DevOps actually changes. DevOps improves how products are built and run; it does not eliminate the need for structured service delivery. Customers still raise incidents, request changes, and need support — regardless of how quickly or elegantly the development pipeline runs.

The real risk is not that DevOps replaces the service function. The real risk is that the two operate in isolation, each moving at its own pace and without awareness of the other’s priorities.

DevOps and ITSM Are Solving the Same Problem

Strip both disciplines back to first principles and you find a shared objective: delivering value to customers. DevOps pursues that goal through faster, more reliable product releases. ITSM pursues it through structured, repeatable service processes that keep customers productive when things go wrong — or when they need help using what DevOps has built.

That common purpose is the foundation for collaboration. Once both teams recognise that they are working toward the same destination, the question shifts from which team is in charge to how do we coordinate effectively.

How DevOps and ITSM Differ in Practice

Understanding the friction points requires acknowledging that the two disciplines operate very differently, even when their goals align.

• Cadence: DevOps teams typically follow Agile methodologies — short sprints, frequent releases, and continuous iteration. ITSM teams are more interrupt-driven. Incidents and service requests arrive unpredictably, so structure and documented processes (often informed by ITIL) are essential to maintaining consistent service quality.
• Scope: DevOps focuses on a defined product backlog — usually a specific set of features or improvements at any given time. The service desk handles the full surface area of everything the organisation has ever shipped, plus everything customers wish it had.
• Feedback loops: DevOps gets feedback through metrics, monitoring, and stakeholder reviews. The service desk gets it immediately and verbally, from customers who are frustrated right now.

Neither approach is wrong. They reflect the different problems each team is solving. The friction emerges when the two operate as separate tracks with no shared visibility.

The Two Pacing Problems That Hurt Both Teams

When DevOps and ITSM are poorly coordinated, two contradictory complaints surface simultaneously — and both are valid.

DevOps moves too fast

A DevOps pipeline that delivers continuous releases is a genuine achievement — but it creates a knowledge gap at the service desk. Agents start receiving calls about features they have not been briefed on. Documentation lags behind the product. Workarounds that worked last week no longer apply. The service desk appears incompetent, not because the team is under-skilled, but because it has been left out of the release loop.

DevOps moves too slowly

From the service desk’s perspective, DevOps can also feel glacially slow. Recurring customer complaints about a broken feature or a missing capability sit in a backlog while the development team works on other priorities. Service agents can only apologise and log tickets; they have no visibility into when a fix will arrive or how to set expectations with customers.

Both problems share the same root cause: a lack of shared information and communication channels between the two teams.

Practical Steps to Align DevOps and ITSM

Solving the coordination problem does not require either team to abandon its working methods. It requires building deliberate communication bridges between them.

1. Keep ITSM informed of the release roadmap

DevOps teams should share upcoming release dates and feature summaries with the service desk ahead of deployments — not after. This gives service agents time to update knowledge base articles, prepare FAQs, and anticipate the questions customers will ask. Even a brief pre-release briefing significantly reduces the knowledge gap.

2. Route service desk intelligence back into the product backlog

The service desk is one of the richest sources of product feedback in any organisation. Recurring incidents, common workarounds, and frequently requested features are signals that the product backlog should reflect. Establish a formal channel — a shared ticketing view, a regular sync meeting, or a tagged ticket category — so that service desk data can inform DevOps prioritisation decisions.

3. Include service representatives in sprint reviews

Inviting a service desk representative to sprint review or demo sessions costs very little. The benefit is significant: service staff understand what is changing before it reaches production, and DevOps teams hear unfiltered customer feedback from the people who talk to customers every day.

4. Align change management processes

ITSM change management processes can feel bureaucratic to DevOps teams accustomed to continuous delivery. Rather than forcing DevOps into a heavyweight change approval board, work together to define a tiered change model — lightweight approvals for standard, low-risk changes; more rigorous review for changes with broad customer impact. This preserves DevOps velocity while maintaining the service governance that ITSM provides.

5. Use shared tooling where possible

When DevOps and ITSM use entirely separate tools with no integration, information silos are almost inevitable. Connecting monitoring and alerting tools to the service desk, or linking incident records to the relevant deployment history, gives both teams shared context and reduces the time spent recreating information that already exists elsewhere.

The Bottom Line

DevOps does not threaten the service function — it makes excellent service harder to deliver without close collaboration. A DevOps team that ships features rapidly but leaves the service desk without briefings, documentation, or a feedback channel will generate customer frustration that the service desk cannot resolve. An ITSM team that insists on heavyweight processes for every change will slow down a DevOps pipeline and create exactly the bottleneck that DevOps was designed to eliminate.

The winning formula is straightforward: treat DevOps and ITSM as two parts of one end-to-end value chain. Break down the silo between product and service the same way DevOps broke down the silo between development and operations. When both teams share information, respect each other’s constraints, and measure success by the same customer outcomes, the result is better for everyone — including the customers both teams exist to serve.

DevOps today plays an increasingly important role as businesses must rapidly develop, deploy, and update applications to keep pace with ever-changing customer demands and market opportunities.

One of the great things about my job is that I get to go to software developer conferences all over the world and listen to people being extremely smart. When you watch enough smart talks, read enough articles, and talk to enough people trying to get stuff done on the ground, it gets easier to spot trends—just like it’s easier to see irrigation patterns from the air than from the ground.

Here are the five trends I think you should watch for in 2020.

See also: Deep Learning Tool Spots Bugs In New Software Hours After Launch

1) Continuous Integration and Continuous Deployment, but not Continuous Release

I was recently at DeliveryConf (which was great, and you should try to go next year, but in the meantime, here is a link to the talks ). At the conference, companies of all sizes and maturity levels described how they were working toward the CI/CD goal of getting code into production more quickly. The hesitation we were all feeling our way around was that we want continuous deployment to production, but most consumer and B2B businesses don’t want to change the user experience that often. Simply put, we don’t want Continuous Release.

In fact, customers frequently resent change, especially when it forces them to retrain users in a new workflow. The thing a user knew how to do automatically is now moved or missing, or there is some new option that no one knows how to use effectively. Interface changes in popular software can mean that companies spend millions of dollars in retraining. Anything that interrupts a user’s unconscious competence and forces them to think about what they’re doing slows them down.

Release is a business decision, and it often is safer and cheaper and better for users if all the changes come at once, so they can all be discussed and taught at the same time. CI/CD, on the other hand, is a technical choice. But that doesn’t mean customers need to experience that cadence, as long as you can deploy without releasing.

2) Leveraging existing workflows

Similarly, there is no reason users should have to learn new workflows just because the tools their software group is using have changed. I think this year, we’ll see a lot of SaaS vendors work with existing enterprise tools to make those tools more powerful, without changing the user experience much, if at all.

I think of this as leverage. It doesn’t matter to a user if a form is backed by a spreadsheet that needs to be manually imported or if it’s wired directly to a CRM. The user has applied the same amount of effort, but the new tooling has moved the fulcrum point, and the user’s work is more effective.

3) Personalization

We don’t all want the same things, as we can tell from the Dark Mode Wars. As our bandwidth and information have changed, so have our expectations about how much we can make our technology spaces personally comfortable.

A great example of this is the Google Now app on Android phones. You can tell it what sports team you follow, and then the app will deliver more news about that team and sport. But it also gives you the option to hide gameday spoilers if you’re not going to be able to watch it right away. They aren’t hiding that information from everyone, or even fans of that team, but they are personalizing the experience by protecting you from knowing the score of the game before watching it.

Personalization gives users more control over their experiences. It also provides more options than would otherwise be feasible to present globally. We can’t be all things to all people unless we allow people to choose which subset of all things they want, and then allow those subsets.

4) Accessibility

The other exciting possibility of increased personalization is better support for different accessibility needs. The US has had web accessibility standards since 2000, but they haven’t been enforced or adopted evenly. That said, we have seen some recent exceptions.

The Supreme Court just ruled against Dominos in a lawsuit alleging that the pizza company failed to comply with accessibility standards. I’m not going to say “this changes everything,” but I will say this might be a good time to be an accessibility consultant who can help teams retool quickly.

The interesting part, and the thing that meshes with personalization, is that different people can have different accessibility needs. Someone with low vision needs solutions that may be incompatible with tab-based navigation, which again may be hard to align with screen readers. Rather than trying to make a single “accessible” page that meets none of those needs well, we’ll use personalization to tune for exactly what different people need.

5) Scientific thinking

This is an interesting outflow of our emphasis on data and metrics. Now that we are doing a better job of democratizing access to statistics and metrics, it’s easier for everyone in the company to understand how changes affect user behavior. Rapid releases and Progressive Delivery make it much easier for us to see how our choices work out in near-real-time. That means it’s possible for anyone—not just the UX team—to see how changes play out. With that visibility, we also can form a hypothesis about how a change will affect the data and then look to confirm or reject the hypothesis.

The scientific method is not heavily taught in most computer science programs, because it wasn’t until recently that we had the fast feedback loop that would make it useful. However, at least in the US, most schoolchildren are taught the basics in elementary school. They learn to ask critical questions like:

What is the current state of the system?
What change am I making?
How can I measure a change’s impact?
Was the impact what I expected it would be?
Do I have any evidence for why or why not?
We need to be able to ask these questions at the team and individual level and get meaningful answers. We can then use those answers to iterate rapidly and stay attuned to what users want and find useful. What’s more, we can avoid spending months building things that virtually no one needs or wants.

Background: Two Announcements from Redis

Towards the end of 2019, Redis made two announcements aimed squarely at its developer community. The first was the release of RedisInsight, a free GUI tool that lets developers manipulate, visualise, and analyse data stored in Redis without leaving a graphical interface. The second was the launch of a Redis Developer Certification Programme, designed to formally recognise proficiency in building applications with Redis.

Howard Ting, Redis’s CMO at the time, spoke to DevOps Online about the reasoning behind both initiatives and shared his broader views on database architecture trends and the DevOps mindset.

Why a Developer Certification?

The certification was conceived as a way to bring consistency and credibility to the Redis ecosystem. Ting explained the core motivation:

“It’s about bringing more consistency and credibility for Redis developers. When someone is looking to hire a developer, they will know that with this certification, they have at least a bare minimum level of credibility and expertise.”

Howard Ting, CMO, Redis

The exam covers the topics Redis considers foundational for effective development:

• Data structures – understanding Redis’s native types (strings, hashes, lists, sets, sorted sets, streams, and more)
• Data modelling – how to structure data appropriately for Redis rather than forcing relational patterns onto a key-value store
• Best practices – patterns for reliability, key naming, eviction policies, and client usage
• Database setup and optimisation – configuration tuning for a given use case

The exam is 90 minutes long, taken online, and is proctored to ensure the credential carries genuine weight. Redis indicated the programme would evolve based on community feedback over time.

Industry Trend: From One Database per Thousand Apps to One App with Many Databases

Ting offered a striking framing of how the database landscape has changed in the microservices era:

“20 years ago we had one database supporting 1,000 applications. Now we have one application that might have 1,000 databases.”

Howard Ting, CMO, Redis

This inversion reflects the polyglot persistence approach that has become common in cloud-native architecture. As microservices decompose monolithic applications into independent, loosely coupled services, each service can—and often should—choose the database type best suited to its specific data access patterns. A session store might use Redis, a product catalogue might use a document database, and financial ledger data might live in a relational database, all within a single application.

The explosion of Kubernetes as a deployment platform has accelerated this trend, making it operationally feasible to run many different stateful services side by side at scale.

Performance Engineering: Design for Elasticity First

On the question of performance and debugging, Ting’s advice centred on understanding the elasticity profile of an application before choosing where data should live:

• Highly elastic applications – workloads with large, unpredictable usage spikes need databases that can absorb sudden load. In-memory databases such as Redis are well-suited here because reads and writes happen at memory speed.
• Predictable, steady-state applications – workloads with consistent throughput have more flexibility in storage tier selection, and the cost advantages of disk-backed storage become more relevant.

Ting also pointed to an emerging two-tier data model that he expected to become standard:

• Hot tier – fast-moving, frequently accessed data stored in DRAM or fast SSDs. Persistent memory (technologies that behave close to DRAM speed at lower cost per GB) was highlighted as a growing option here.
• Cold/archival tier – slower-moving or historical data stored on traditional disk or flash-backed databases in the back end.

The practical takeaway for developers designing data-intensive, highly personalised, or real-time reactive applications is to start with an in-memory database as the primary data layer rather than retrofitting one later.

Optimising Data Management: Pick the Right Tool and Plan Long Term

Ting distilled data management advice into two principles:

1. Pick the right tool for the job. No single database is optimal for every access pattern. Evaluate whether your data’s characteristics—size, access frequency, query complexity, latency requirements—align with the database you are considering.
2. Plan for where the application is going, not just where it is today. Ting warned against choosing a solution that creates a dead end, forcing a complete application refactor once scale or data model requirements change. He encouraged thinking in multi-year horizons when evaluating platforms.

DevOps and Automation: The Core Infrastructure Must Be Stable

Asked how Redis itself was applying DevOps principles internally, Ting’s answer was grounded in the relationship between automation and developer velocity:

“It’s just not intuitive of the DevOps mindset to continue to deploy new features and capabilities quickly [without stable infrastructure]. You want to have the core infrastructure be as stable, solid, robust, and resilient as possible.”

Howard Ting, CMO, Redis

The argument is straightforward: if teams are manually handling routine operational tasks—sizing, deployment, upgrades, scaling, and recovery—those are human cycles that cannot be spent on higher-value work. Automating the mundane creates the headroom that allows continuous delivery to function sustainably.

This is a commonly cited principle in the DevOps community, and it aligns with the broader industry push towards infrastructure as code, self-healing systems, and operator patterns for managing stateful workloads in Kubernetes environments. Redis has invested in managed cloud offerings and Kubernetes operators to push operational complexity down the stack, away from application developers.

Key Takeaways for DevOps and Platform Engineers

• Polyglot persistence is not a trend to resist—it is a natural consequence of microservices decomposition. Plan your platform to support multiple database types.
• Design your data tier around your application’s elasticity profile. High-variability workloads benefit disproportionately from in-memory storage.
• A two-tier hot/cold data architecture is a pragmatic way to balance cost against latency requirements as persistent memory options mature.
• Automating infrastructure operations (scaling, recovery, upgrades) is a prerequisite for sustainable continuous delivery, not an optional enhancement.
• Developer certification programmes can raise hiring confidence in communities built around open-source tools—but verify what a specific certification actually tests before treating it as a proxy for seniority.

Where is DevOps headed this year? Let’s examine key trends around topics including training, metrics, and tools

In 2019, we saw numerous successful DevOps initiatives across large and small enterprises. If you were involved in one, congratulations! But what can we expect for the future of DevOps in 2020?

As thought leaders in the DevOps community, we believe the following ten trends will shape the next year of DevOps across the globe.

1. Agile and DevOps will increase collaboration between technology and business functions
Agile and DevOps are grassroots movements that started within technology. In many cases, however, Agile and DevOps have not been able to break out of technology. On the other hand, Agile has been adopted in other functions, including finance, human resources, procurement, and marketing. Some senior leaders are increasingly inviting their whole organizations to “become agile.”

However, this doesn’t seem to have helped the technology community join forces with their colleagues in these other functions. Because of the competitive pressure digital is putting on organizations, in 2020 we will start to see more collaboration across functions – with agile as the conversation starter.

Encourage your teams to talk to people from different functions about their experience using agile methods.
To speed this up, encourage your teams to talk to people from different functions about their experience using agile methods. Some helpful questions include: How are you doing agile? What are you doing? What is changing for you? What issues are you having? How could we work together to help to address some of these issues? These questions will help people from different functions get to know each other as people, and collaboration will improve.

[ Are you fighting skeptics? Read also: DevOps for doubters: How to deal with 9 kinds of people who push back. ]

2. Training and improving DevOps skills will become an organizational priority
DevOps requires trying out new technologies. Recent research from the DevOps Institute found that 55 percent of survey respondents prefer to hire into their DevOps teams from within their organization. Unfortunately, many companies don’t have the necessary skills to do this, and hiring new people might not be possible due to budget restraints.

One approach is to create internal training universities. This is what the courier delivery services firm FedEx did: The company knew it did not have adequate skills in its talent pool of engineers, and this led its CIO to initiate the FedEx Cloud Dojo, which teaches its own engineers modern software development and technologies and functions as a university for FedEx. The university has reskilled more than 2,500 software programmers.

Organizations that want to use DevOps to help advance their digital transformation must make drastic improvements in training, learning, and improving skills that are essential to DevOps. We expect to see a more proactive pursuit of this in 2020.

3. Upskilling and cross-skilling will lead to the rise of the T-shaped professional
Recognizing the strained talent market, organizations and individuals will invest heavily in upskilling and cross-skilling in order to meet accelerating demands for new skills. While all IT professionals will need to become more cross-domain competent, developers, in particular, will need to add new breadth to their skills portfolio in areas such as testing, containerization, infrastructure, AI, and security.

Developers in particular will need to add new breadth to their skills portfolio.
There will also be a stronger emphasis on core (soft) skills such as empathy, customer experience, and collaboration. Silos are starting to come down in many areas, and the need for everyone to become T-shaped, with depth and breadth of knowledge, will become necessary to enable and support innovation. All of this training and new collaboration (see Trend 1, above) will lead to more workers developing new technical and professional skills and personal qualities, adding new depth and capabilities to the individuals on your teams.

4. More teams will shift mindsets from “job done” to “value realized”
Value Stream Mapping can help change the way your teams think about the definition of done (DoD) from being “I did my job” to “the value is realized.” It is one of the most effective ways of changing behaviors and getting your teams to think about the end-to-end lifecycle of what they’re working on.

This is why the adoption of Value Stream Management is critical in 2020. It will enable you to automate the outputs from Value Stream Mapping for ongoing progress monitoring. This lets a team connect all parts of the ever-complex DevOps toolchains with system-derived data based on cycle time. Teams that adopt Value Stream Management in 2020 will be able to base their next improvement experiments on data-driven decisions and prioritizations.

[ Read also: DevOps terms: 10 essential concepts, explained. ]

5. Tool fatigue will worsen before it gets better
The number of tools and frameworks in technology is daunting. The challenges IT teams face to understand, interconnect, and apply these will continue, and in 2020, there is no real resolution in sight.

The competition in the DevOps toolchain is fierce and flourishing.
The competition in the DevOps toolchain is fierce and flourishing. Events and conferences are filled with technology and best-practice sessions. Books, blogs, and videos are flooding email inboxes, and thought leaders are eager to share their expertise. Additionally, more open source tools are emerging to integrate new technologies.

To survive the challenges of complexity, it’s becoming increasingly important to have an automation strategy. As you work to develop this, don’t lose sight of the actual problems you are trying to solve and how you can get there by leveraging your own teams.

6. DevOps will become more measurable and metrics will become better defined
“What gets measured gets managed.” This quote is still valid, more than 60 years after Peter Drucker referenced it in his book, “The Practice of Management.” What we all want to avoid, however, is measuring for metrics’ sake.

Avoid the syndrome of measuring for metrics’ sake.
For the next few years of DevOps, the continuous improvement metrics we know today will continue to be the key metrics that matter. In 2020, we expect more organizations will agree on what to measure and adopt these metrics. Those looking for support can lean on the performance metrics outlined in research from DevOps Research and Assessment (DORA), which cites five measures of Software Delivery and Operational Performance (SDO) that can be used as leading indicators of success for high-performing DevOps teams. Key benchmarks within the report provide guidance on areas teams must improve in 2020. Selecting these key metrics and populating them with data will provide insights into the value and journey of DevOps.

7. DevOps teams will earn more and experience increased job satisfaction
Employees who continue down the DevOps path in 2020 will see benefits that go directly into their wallet and their job satisfaction. Automation will allow employees to work on more value-adding work instead of mundane manual tasks, resulting in improved job satisfaction and reduced stress levels.

In particular, DevOps engineers who see their work in automating and collaborating for improved software delivery can expect their salary to be significantly higher than peers in traditional roles, such as system administrators. Investments in training and certifications will also positively impact the quality of code and therefore may improve the business results. This, in turn, might finally change the value equation within more organizations and give IT more strategic seats at the table. It will certainly improve how other functional areas work with IT now and in the future.

8. There just might be service management and DevOps reunification
With the recent release of ITILv4, 2020 will be an interesting year for organizations that have been adopting DevOps and service management frameworks. The development and management of software products needs agile techniques, with a focus on value co-creation in a way that reduces waste. DevOps, service management, and other best practices like SRE can coexist to align teams, meet stakeholder demands, and improve the value delivered. Because digital transformation is not achieved instantly across an organization, established companies should begin with best practices and methodologies that are suited to their needs by starting small – then learn, build expertise, and scale up.

[ Read also: DevOps vs. ITIL 4 vs. SRE: Stop the arguments. ]

9. A new generation of IT takes over
The number of people who remember the days before DevOps is starting to shrink. The younger generation of workers on today’s IT and DevOps teams don’t remember the strict silos, with clear lines around areas of responsibility such as infrastructure, operations, application design, development, testing, and security. They don’t remember how this caused a lot of transition work between teams and groups. They don’t know that product owners, business analysts, architects, developers, testers, release managers, system administrators, and infrastructure owners had to agree and coordinate on the planning, development, testing, deployment, operation, and management of a piece of software. Just typing that sentence was exhausting; imagine living it.

The number of people who remember the days before DevOps is starting to shrink.
When we celebrated the 10-year anniversary of DevOps in 2019, we saw the wall removed between Dev and Ops. This is a reason for all of us to celebrate, and in 2020, expect to see this happen as more and more organizations adopt DevOps.

10. Artificial Intelligence adoption will rise, and DevOps teams should check it out
AI and machine learning (ML) were recently rated as the most important enterprise technologies of the next decade, according to a recent report from ISACA. Both will play a critical role in next-generation IT operations and DevOps teams. AIOps will give DevOps teams the ability to analyze more data faster, allowing them to improve key processes, tasks, and decision-making. In 2020, expect to see more DevOps teams adopting these tools, which automate the ingestion of fast volumes of data, use ML to analyze the data, and have the ability to leverage knowledge for automation or decision-making.

The AIOps market has already gained momentum, with 22 percent of IT enterprises using ML and data sciences as part of their work. The vendor landscape is broad, with a variety of leaders. This year, the AIOps market will continue its shift from a science project to the pilot and experimental stage.

MORE ON DEVOPS
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Much work remains in 2020 to aid in the future of DevOps adoption. It will require more than focusing on tools and techniques. DevOps initiatives must be registered as change programs, requiring a time, resource, and priority commitment from all business leaders. We’re hopeful that some of these trends will be realized this year, and that DevOps will be recognized as a new way of working.

Most developers come from backgrounds in computer science, engineering, or mathematics. These disciplines train people to decompose complex problems, reason about systems, and iterate toward solutions—precisely the skills organizations need when navigating transformational change. Giving developers a meaningful voice in business decisions is not just good people management; it is a competitive advantage.

To unlock the full value of developer talent during a digital transformation, organizations should focus on four key areas.

1. Encourage Personal Development

A growing number of developers are actively investing in communication and leadership skills, recognizing that technical ability alone no longer defines a successful career. Soft skills are essential for building internal relationships, advocating for the engineering team’s work, and engaging empathetically with end users and customers.

Consider two common scenarios: a stakeholder who needs to understand how a product team operates, and a client who needs hands-on guidance when adopting a new solution. In the first case, a developer must be able to translate technical decisions into business language. In the second, they must apply empathy and patience—skills that go well beyond writing code.

Organizations that offer structured learning and development programs—covering new programming languages, emerging tooling, and interpersonal skills—are more likely to attract developers who are motivated to contribute across the business, not just within their immediate team.

2. Keep Things Agile

Working within an Agile framework provides developers with a built-in mechanism for developing soft skills through regular ceremonies such as sprint planning, retrospectives, and daily standups. Each meeting gives team members a structured opportunity to communicate blockers, offer solutions, and take ownership of outcomes.

This open, iterative approach stands in sharp contrast to the traditional Waterfall model, where siloed teams hand off deliverables with minimal communication and feedback arrives too late to be actionable. In Agile teams, shared responsibility is the norm: a developer may need to understand a UX decision, a tester may flag an architectural risk, and a business analyst translates all of this into stakeholder updates.

Agile also surfaces natural leaders. Developers with an inclination for leadership can demonstrate it organically through cross-functional collaboration, rather than waiting for a formal promotion. The relatively small size of most Agile teams means individual voices are heard and feedback loops are short—making learning on the job practical and continuous.

3. Be Clear on Requirements and Standards

Attracting strong developer talent is only part of the equation. Organizations must also be transparent about daily expectations—both technical and behavioral. Clear requirements reduce ambiguity, minimize rework, and allow developers to focus energy on work that genuinely moves the business forward.

On the technical side, efficient and well-tested code should be the goal. Workarounds and technical debt may resolve immediate problems, but they introduce systemic risk—particularly when defects surface in production or a client encounters unexpected behavior. Fewer bugs in a sprint naturally shifts scrum meeting focus from firefighting to strategy and innovation.

Organizations should establish documented coding standards, code review processes, and definition-of-done criteria to reduce the temptation of quick fixes and ensure releases are production-ready. Developers must also be comfortable working from design specifications and incorporating client feedback quickly—especially in teams operating continuous integration and continuous delivery (CI/CD) pipelines.

An interest in emerging technologies is valuable, but so is the pragmatic ability to leverage existing solutions rather than rebuild them from scratch. Reuse reduces cost and protects return on investment in established platforms.

4. Keep an Eye on the Future

An organization’s long-term competitive position increasingly depends on how quickly it can respond to technology advances. This means developers need the freedom—and the time—to explore new tools, experiment with emerging technologies, and bring innovation back into the product roadmap.

Technologies such as IoT and connected devices, AI and machine learning, augmented and virtual reality, blockchain, and 5G networks are already opening new business models and user experiences. Developers who build hands-on expertise in these areas become more than implementers—they become advisors who can identify viable use cases, flag risks early, and propose solutions that business stakeholders may not yet know are possible.

This creates a virtuous cycle: developers who grow their skills make themselves more valuable to employers, and employers who invest in that growth gain advocates who can connect technical decisions to strategic outcomes. In a digital transformation, that connection is everything.

Conclusion

Developers are not just executors of a digital transformation strategy—they are among its most capable architects. Organizations that create the conditions for developers to grow personally, work collaboratively, operate to clear standards, and stay ahead of technology trends will find that their engineering teams become a genuine driver of business change, not just a cost center delivering features.

This article is based on original reporting from devops.com.

Cloud and Containerization Skills Are in High Demand

Jobs platform Indeed.com analyzed internal job-posting data to identify the biggest macro-trends shaping the tech hiring market. The findings, published in the Indeed Tech Skills Explorer: Big Picture Tech Skill Trends report, covering activity from September 2014 through September 2019, pointed to three dominant themes: the rise of cloud computing and containerization, rapid JavaScript innovation, and surging demand for Big Data skills.

The headline finding for DevOps practitioners is straightforward: containerization and cloud skills are no longer niche capabilities — they have become standard requirements across a wide range of tech roles.

Docker and Kubernetes: The Fastest-Growing Containerization Skills

Indeed’s data identified Docker as the single fastest-rising tech skill over the five-year period ending September 2019, increasing more than 40x in job posting mentions between September 2014 and September 2019. The report’s explanation for this growth aligns with how practitioners use the technology:

“Docker, Kubernetes, and other containerized tech tools allow software engineers to isolate the environment in which an application runs, permitting rapid scalability as user traffic grows. Employers increasingly demand these skills.”

Indeed Tech Skills Explorer

Kubernetes, while newer to mainstream adoption than Docker, showed the steepest recent trajectory. In the single year ending September 2019, Kubernetes ranked ninth among all skills with the fastest growth — making it the most widely used tool on that particular fast-growth list.

AWS and Azure Become Standard Infrastructure Skills

Cloud platforms followed a similar trajectory. According to Indeed’s data:

• AWS became the sixth most commonly cited overall tech skill in job postings.
• Microsoft Azure ranked 17th overall.

Indeed summarized the shift clearly: cloud computing has moved from a cutting-edge, specialist skillset to a standard and widespread part of the workflow for many tech employees. For hiring managers, this means cloud fluency is increasingly a baseline expectation rather than a differentiator.

JavaScript: Angular, React, and Node.js Lead the Pack

The second major trend Indeed identified is continued rapid innovation in the JavaScript ecosystem. Key data points from the report:

• Angular held the top position as the most commonly required JavaScript library, having surpassed jQuery sometime in 2018.
• jQuery declined roughly 30% over five years, while Ajax dropped even further, down approximately 54%.
• React.js grew sharply over the five-year window and recently overtook jQuery as the second most popular JavaScript library in job postings.
• Node.js grew steadily throughout the period.

The takeaway for full-stack and front-end engineers is that the JavaScript landscape continues to evolve quickly. Skills tied to older patterns are losing ground, while component-based and server-side JavaScript skills remain in strong demand.

Big Data Tools and Data Science Programming Languages Surge

The third trend Indeed highlighted is the rise of data science and Big Data processing skills. Specific findings include:

• Apache Spark grew more than 10x since 2014 — described as “particularly stunning” growth.
• Amazon Redshift and TensorFlow (Google’s machine learning library) also showed steep upward trajectories.
• Python rose 123% over the five-year period and an additional 12% in just the final year.
• R more than doubled over five years, with a 6% gain in the final year alone.

As organizations generate and store more data, the programming languages and distributed processing frameworks purpose-built for data work are commanding a larger share of job descriptions — directly influencing what skills engineering teams are expected to hold.

What This Means for DevOps and Platform Engineering Teams

Taken together, the three trends Indeed identified reflect a broader shift in what production software infrastructure looks like. Containerized workloads running on managed cloud platforms, served by modern JavaScript front-ends, and backed by scalable data pipelines, have become the dominant architectural pattern. The skills required to build and operate these systems — Docker, Kubernetes, AWS, Azure, Python, Spark — are now core competencies rather than specializations.

For individuals, this means that investment in container orchestration, cloud-native tooling, and data engineering fundamentals is likely to offer the strongest return in the job market. For engineering managers, the data reinforces the importance of structured upskilling programs that cover these foundational technologies before they become critical hiring gaps.

Report Methodology

Indeed’s analysis queried job descriptions posted on Indeed.com between September 2014 and September 2019 using a list of more than 500 tech skill terms. Only US English-language postings matching a defined set of tech job titles were included. The report notes one methodological caveat worth keeping in mind:

“Multiple skills can appear in the same job description and some terms can appear in multiple distinct skills, like ‘SQL’ and ‘SQL Server.'”

Indeed Tech Skills Explorer methodology note

The Tech Skills Explorer report this post draws on was the third of four parts in Indeed’s broader interactive series examining which tech skills are in demand and which job roles are using them.

Conclusion

Indeed’s job-market data through late 2019 painted a clear picture: cloud computing, containerization, modern JavaScript, and data science tooling had all reached an inflection point where employer demand far outpaced available talent. The underlying forces driving these trends — distributed systems, scalability requirements, and the explosion of data — have only intensified since this data was collected, making these skills as relevant today as they were at the time of the original report.