As a skills category, automation now runs the gamut of virtually any and every IT job title.
From “traditional” IT positions (such as software developer, system administrator, or security analyst) to more recent titles like DevOps engineer, cloud platform engineer, or site reliability engineer (SRE), automation skills are relevant – and often required – in a great many tech roles today.
So if you’re looking to bolster your appeal on the IT job market, building automation skills is certainly a good way to do it. Automation chops have the added bonus of portability: If you don’t want to be pigeonholed into a particular title or function, automation practices and technologies may pave the way into other roles down the line, too.
7 automation IT job skills
“Automation skills” is a broad category, of course. Let’s dig down into seven subsets of IT automation – including non-technical attributes that pair well with the relevant technology skills – that IT leaders and recruiters say are in high demand at the moment.
1. DevOps-centric tools and practices
DevOps and automation are deeply linked; as a result, many of the skills and technologies commonly associated with DevOps are also in demand for automation-focused roles in general.
“DevOps has become the standard methodology for software development as well as cloud deployment, so those who do not understand the principles and practices of DevOps will struggle,” notes Clyde Seepersad, SVP and GM, training and certification, The Linux Foundation. “More and more professionals are realizing this and that combining Kubernetes and Linux technologies with DevOps practices leads to superior results. I only see this realization expanding further in the future.”
If you have solid DevOps experience, that’s going to appeal to many hiring managers who are looking to bolster their automation capabilities. From a tooling perspective, one particular area seems to stand out: Infrastructure as code.
[ Understanding automation: What is Infrastructure as Code? ]
“One of the most important approaches to automation is infrastructure as code,” says Chris Nicholson, head of the AI team at Clipboard Health. “Infrastructure as code makes it easier to spin up and manage large clusters of compute, which in turn makes it easier to introduce new products and features quickly, and to scale in response to demand.”
Kelsey Person, senior project manager at the recruiting firm LaSalle Network, agrees: Experience with infrastructure as code (and other DevOps tools) pops on a resume right now, because it indicates the knowledge and ability needed to help drive significant automation initiatives elsewhere.
“One skill we are seeing in more demand is knowledge of DevOps tools, namely Ansible,” Person says. “It can help organizations automate and simplify tasks and can save time when developers and DevOps professionals are installing packages or configuring many servers.”
[ Download the eBook preview: Ansible for DevOps ]
2. Scripting languages
The ability to write homegrown automation scripts is a mainstay of automation-centric jobs – it’s essentially the skill that never goes out of style, even as a wider range of tooling (such as certain robotic process automation (RPA) software and low-code or no-code tools) enables non-developers to automate some previously manual processes.
As Ansible senior principal product manager Chad Ferman wrote recently over at Enable Sysadmin, “Having the ability to script proficiently with your platform’s built-in language (PowerShell for Windows or Bash for Linux) is a great place to start” when building IT automation skills.
[ Read also: 8 skills you need to be successful in IT automation. ]
Indeed, those are good starting points, but there are multiple automation-friendly languages that could be useful in a variety of roles.
Ferman points to Python as a universal language for increasingly sophisticated automation work. It has become a go-to in data science, for example, but the language can apply to a wide range of automation use cases.
“Another critical skill is writing Python scripts for Web scraping to quickly amass and organize useful data,” Nicholson says. “Our recruiting team does that because we have such ambitious hiring needs. The landscape of data about who’s looking for work is really fragmented and siloed.”
3. Containers and Kubernetes
Kubernetes has become a heavyweight anchor in the sea of cloud platforms and tools. Its adoption and usage continues to rise because (among other reasons) the containerization trend continues unabated. Around 75% of global companies will be running containerized applications in production in 2022, according to Gartner. Managing containers in production at any kind of scale quickly requires orchestration, of which automation is a significant piece.
[ Related read: 5 Kubernetes trends to watch in 2022. ]
And 72% of the IT leaders surveyed as part of Red Hat’s 2021 Global Technology Outlook said that they expect their container usage to increase.
This means there’s a corresponding demand for Kubernetes talent – and the supply simply isn’t there yet. As Seepersad recently told us, the hunt for IT pros with Kubernetes skills is fierce. “There’s no sign of slowing in terms of Kubernetes – and cloud-native generally – adoption.”
4. Testing automation
Automating testing is a huge piece of the broader shift to CI/CD pipelines – sure, the acronym means continuous integration and continuous delivery (or continuous deployment), but it might as well be called “continuous automation.”
Mike Mason, global head of technology at Thoughtworks, says that while his consultancy tries to stay agnostic about specific tools, testing automation is a crucial category. Without it, teams will be much harder pressed to ship fast and frequently – twin goals that seem to pervade the software world today.
“Automation testing skills are very important, whether that’s testing traditionally built software or testing RPA or low-code solutions,” Mason says. “The ability to automatically test new versions of software is key to achieving Continuous Delivery and getting value into production quicker.”
Automating testing (as with integration, vulnerability scan, and so forth) is key to ensuring quality and reliability without introducing bottlenecks. You don’t have to be running a mature CI/CD pipeline to apply this principle.
This skill area also intersects with #2: If you can write your own tests, highlight that as relevant.
5. Security automation
“Security will continue to be a priority for technology teams, which means more organizations are looking for employees with security automation experience,” Person says.
Security automation is a massive category unto itself. (To that end, check out IT security automation: 3 ways to get started.) It can mean everything from automated runtime security scans to automated remediation of lower-tier incidents to entire processes and toolchains to whole platforms for managing an organization’s security programs.
The security industry is flush with acronyms, but two of them currently stand out on the job market in terms of automation experience, according to Person: “Companies specifically are seeking candidates who have experience with SOAR (Security Orchestration, Automation and Response) and SIEM (Security Information and Event Management) tools, which both can help organize data, recognize threats and automate the response to that threat.”
6. Algorithmic thinking
If you’re an experienced machine learning engineer, then you already understand and work with algorithms. Software engineers in general have a knack for “if-then” logic and processes. As automation use cases broaden, however, Nicholson sees the ability to “think in algorithms” as a growing soft skill for more people, including non-technical people or IT pros whose job doesn’t require writing a lot of code.
“By that, I mean you need to think like a computer, in small, precise steps walking through a decision tree: If X happens, then Y should happen,” Nicholson says. “Software engineers do that for a living, but non-software engineers are capable of it, and it makes their lives a lot easier.”
Indeed, this is a skill development area that could apply widely in many organizations – a finance pro who wants to use RPA to automate repetitive drudge work, or a business or process analyst who wants to boost their value by learning to “speak IT” – and therefore become a better translator between business and technical requirements.
“If you want to tell computers what to do, you need to learn to think like they do,” Nicholson says.
7. Communication, openness, and integrity
Automation in the absence of “soft” qualities such as communication, openness, and integrity is more likely to cause problems.
When leadership doesn’t effectively share its big-picture strategy (and how automation fits), for example, that can stoke job-loss fears and other automation anxieties. And that might be just the beginning of a much steeper downside.
As a result, hiring managers are increasingly looking for people who pair those kinds of principles and traits (like communication or integrity) with their technical expertise. For similar reasons, these characteristics are also important in AI/ML, where issues like bias could have major consequences.
“One of the challenges of automation work is that it is a newer field for a lot of organizations, so there are many unknowns throughout the process,” Person says. “Companies need employees who will openly discuss any difficulties of these projects.”
While IT automation isn’t new, many of the ways it is being applied (and the tools for doing so) are relatively recent – so the know-it-all attitude isn’t probably going to show up on many lists of sought-after skills.
“Everyone is learning about these tools and processes, so effective communication that doesn’t hide information will be key in tackling problems and ensuring projects stay on track,” Person says.
[Where is your team's digital transformation work stalling? Get the eBook: What's slowing down your Digital Transformation? 8 questions to ask.]
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