The DevOps skills landscape does not stand still. According to the DevOps Institute’s annual Upskilling Reports, the most valued skills shifted noticeably between 2019 and 2020: automation, cloud, and soft skills such as communication and empathy topped the 2019 list, while governance, risk and compliance (GRC), process knowledge, and knowledge management rose sharply in importance just twelve months later. For any organization trying to keep its engineering workforce current, this pace of change turns upskilling into a continuously moving target rather than a one-time initiative.
The widespread shift to remote and distributed work has further widened the gap. Distributed teams rely more heavily on asynchronous collaboration tools, make more frequent changes to shared code bases, and must integrate work across more diverse environments—all of which demands a broader and more fluid skill set than a co-located team might require.
Organizations that successfully close the IT skills gap can accelerate their transformation efforts and build the organizational resilience needed to weather future disruptions. Doing so requires three things: understanding why specific skills are rising in demand, selecting tools that reduce the burden of constant retraining, and cultivating a lasting culture of learning.
Why These Skills Are Rising in Demand
Before investing in any upskilling program, it pays to understand the forces driving skill requirements. Without that context, training budgets get wasted on skills that may fade from relevance quickly.
Governance, Risk, and Compliance (GRC)
The surge in demand for GRC skills is a direct consequence of an increasingly complex regulatory landscape. Regulations such as the EU General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) require that privacy and risk controls be woven into software from the ground up—not bolted on after deployment. DevOps teams do not necessarily need to become legal experts, but they do need to internalize privacy-by-design and security-by-design philosophies. Developers who understand data classification, consent management, and audit logging are better equipped to ship compliant software without depending entirely on specialist compliance teams at release time.
Process and Knowledge Skills
The growing emphasis on process and knowledge skills tracks closely with the broader adoption of Site Reliability Engineering (SRE) practices. SRE introduces discipline around service-level objectives, error budgets, alert monitoring, and escalation runbooks—all of which demand explicit process thinking. Engineers who invest in understanding on-call workflows, incident response playbooks, and blameless postmortem practices will find these skills increasingly transferable across organizations as SRE continues to mature.
Collaboration and Integration Skills
As distributed teams become the norm, the ability to collaborate effectively through asynchronous tooling matters more than ever. Higher volumes of pull requests, code reviews, and merge conflicts are practical indicators that teams need stronger skills in version control workflows, branch strategies, and CI/CD pipeline hygiene. These are not glamorous skills, but they compound in value as team size and code base complexity grow.
Choosing Tools That Reduce Skill Variability
Because in-demand skills can shift from year to year, organizations that build their workflows around tools requiring constant retraining face an uphill battle. A more sustainable approach is to invest in tools that abstract away repetitive complexity and remain stable across different environments.
- Container orchestration for multi-cloud portability: Many organizations now run workloads across multiple cloud providers to maximize availability and avoid vendor lock-in. Container-based workflows—using platforms such as Kubernetes—let teams deploy consistently across cloud environments without needing deep provider-specific expertise for every project. This reduces the surface area of skills that must be maintained per cloud vendor.
- Automation for compliance and governance tasks: Manual compliance checks, data-privacy audits, and risk assessments are time-consuming and error-prone. Tools that automate policy enforcement, audit trail generation, and vulnerability scanning let engineers focus on product work rather than on learning the specifics of each regulatory framework by hand.
- Low-friction CI/CD pipelines: Pipelines that are well-documented, version-controlled, and designed with sensible defaults lower the learning curve for new team members and reduce the cognitive overhead of onboarding to new projects.
The principle here is straightforward: every task that a tool can handle reliably is a task that no longer needs to be a human skill requirement. This does not eliminate the need for upskilling, but it does let engineers concentrate their learning energy on higher-order, durable capabilities.
Building a Sustainable Culture of Learning
Technology alone cannot close the skills gap. Organizations also need deliberate cultural practices that encourage continuous learning, even—and especially—in distributed or remote environments where informal knowledge transfer is harder to facilitate.
Leadership Commitment
Learning culture starts at the top. Engineering managers and department leaders who actively protect time for training—blocking out hours for online courses, funding conference attendance, and visibly participating in learning themselves—signal that skill development is a legitimate use of work time, not something engineers must squeeze into personal hours. Without this signal, upskilling programs often atrophy despite good intentions.
Structured Learning Events
Ad hoc learning is valuable but insufficient on its own. Structured events create shared context and reinforce a team-wide growth mindset. Consider incorporating:
- Internal hackathons: Time-boxed, problem-focused events that encourage engineers to experiment with unfamiliar tools or domains in a low-stakes setting. Hackathons reinforce agile thinking and surface creative solutions that routine project work rarely produces.
- Tech talks and internal demos: Short, regular sessions where team members share what they have learned—from a conference talk, a new library, or a post-incident review—democratize knowledge and reduce information silos.
- Blameless postmortems: Beyond their operational value, postmortems are a structured learning mechanism. Reviewing what went wrong, why, and how processes will change transforms incidents into durable institutional knowledge.
- Mentorship pairing: Pairing senior engineers with less experienced colleagues on specific skills—even asynchronously—accelerates knowledge transfer in ways that courses alone cannot replicate.
Continuous Process Inspection
Agile and DevOps both emphasize continuous improvement, and this principle applies equally to the learning process itself. Teams that regularly reflect on which skills are proving most valuable, which training investments delivered results, and where knowledge gaps are causing friction will be better positioned to adapt their upskilling strategy as requirements evolve.
A People-Process-Technology Framework for Closing the Gap
Closing the DevOps skills gap is not a one-time project. Technologies such as multi-cloud infrastructure and edge computing are maturing rapidly, and each wave of adoption brings new skill requirements in its wake. Organizations that treat upskilling as a sustained, strategic priority—rather than a reactive response to individual job vacancies—will build more adaptable engineering organizations over time.
The practical path forward combines three elements working in concert:
- People: Invest in individuals through structured learning programs, mentorship, and leadership that models continuous development.
- Process: Embed learning into everyday workflows through postmortems, retrospectives, and structured knowledge sharing.
- Technology: Select tools that reduce skill complexity and automate repetitive tasks, freeing engineers to focus on durable, high-value capabilities.
Simply telling developers to learn new skills without explaining why those skills matter—or without providing the time, resources, and tooling to support that learning—is a short-sighted and ultimately ineffective approach. The organizations most likely to thrive are those that understand the root causes of shifting skill requirements and equip their teams accordingly, building not just competency for today, but adaptability for whatever comes next.
Source: This article draws on themes originally reported at insights.dice.com and the DevOps Institute’s Upskilling Reports.