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What skills are essential to becoming a successful Data Engineer?



Organizations across every sector are realizing the importance of data and maintaining strong data operations. Its recognition as a powerful business asset has seen the emergence of dedicated data teams comprising full-time roles for data scientists, architects, analysts, and, crucially, data engineers. In this chapter, we’ll take a look at how aspiring professionals can take that all-important step onto the data engineer career ladder.


Data engineering encompasses many overlapping disciplines. It is hard to chart a single route to becoming a data engineer. Studies in areas like information and communications technology (ICT) or software engineering will help, but I’ve also seen amazing data engineers with degrees in physics and mathematics. Career paths are also varied. Strong data engineers on my team have joined from roles as diverse as sales, operations, and even marketing. As long as you have basic experience in writing small scripts and running data-cleansing projects, you should be ready to take your first steps into data engineering.


So, if background isn’t so important, what are some of the skills an aspiring data engineer needs to succeed? Three standout skills will give you an important head start.


The first of these is solid experience across the software development life cycle. I may be a bit biased here, given my background as a software engineer, but the skills that working across a software development life cycle gives you are invaluable in the world of data engineering.


Second is knowledge of how to properly use SQL and a good grasp of at least one other static and one dynamic programming language. This might seem basic, but it can’t be overstated just how much organizations rely on SQL in their data operations. Combining this with an understanding of how to work with, for example, Python and Rust will give you an important grasp of how great software is built and, ultimately, how it can be applied to the world of data.


The third foundational skill is dependent on the sub-role of data engineering you want to specialize in. For those looking to specialize in data processing, developing your understanding of data storage technologies, as well as continuing to hone your skills with SQL, is key. For those who want to go down a more traditional software engineering route, honing your analytical skills will be crucial, as your main focus will be on big data projects. The bottom line here is that you should decide early on which area you want to focus on and develop your skills to complement that function.


My final piece of advice applies to every level, from aspiring data engineers to established software engineers looking to take the next step up: get into open source! If you’re learning how to build and having fun with open-source data engineering, you’re adding to your repertoire of skills. The best way to advance in your data engineering career is to use open-source tools.


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