Data engineering is a fascinating field. You get to work with a variety of interesting data, cutting-edge technologies, as well as with diverse teams of data professionals and domain experts. The entire field of data engineering is relatively new. As a data engineer, your role is crucial to the company’s success — many data professionals, including data analysts and data scientists, rely on you in order to do their work. You are responsible to equip them with data that is always available, reliable, and in a proper structure.

The companies need you to make informed decisions based on real data and KPIs generated from it. And they are willing to pay you well if you are good at it! Let’s look at what skills are in high demand, what factors play a large role in future career prospects, and how to approach the technical interview.

The skills in demand

Overall, it’s usually hard to give any _truly general _advice but I summarize the skills that seem to be the most relevant, from what I saw being mentioned numerous times in job ads and from my experience in the field.

1. Being a T-shaped professional

It’s considered best to aim for being a **generalist **(the horizontal bar in T) in the sense that you understand the general concepts of databases, cloud computing, data warehousing, big data, and that you know at least some basics of SQL, Python, Docker, and creating ETL.

At the same time, you should have **stronger skills in at least one particular area **(the vertical bar in T). For instance, you might be really good at writing **Spark **or **Dask **data manipulations or you may have some particular domain knowledge required by the company you apply for, which sets you apart from other applicants.

_In many cases, **knowing SQL well****+ basics of Python, Linux and AWS _**can already get you to a fairly-paid junior position.

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