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How to thrive and evolve as a Data Engineer

How to thrive and evolve as a Data Engineer

How to thrive and evolve as a Data Engineer

Today, data generated by the most diverse applications is the raw material that powers all industries and helps them make better business decisions. Managing, maintaining, and saving data are tasks that are part of a Data Engineer’s daily life.

There are many positions in the data industry, and it’s easy to lose focus on the ones that are exactly what we do, even when we’ve been working in the field for a while. In addition to us Data Engineers, there are also Data Scientists and Data Analysts and in some companies the line between these roles turns out to be quite tenuous. That said, my first Tip arrives exactly from this question:

1 – Seek to acquire technical knowledge that goes beyond your role.

As a Data Engineer, you should not only seek to be an expert in your field, but you should also try to keep up to date on the roles in adjacent positions (Data Science and Data Analysis). The fact that you have knowledge and/or technical skills in these other areas makes communication between teams more effective. And, in addition, it makes you have a greater understanding of the specifics of each area in a clearer way, which will also help you stand out in your career.

2 – Be a good Team Player.

A Data Engineer must actively collaborate with Data Scientists and Analysts, as well as with the business areas, in order to be able to build solutions that meet, or even exceed, the company’s needs and goals. Often, during pipeline design, there is a discovery phase where it is necessary to talk to the various business areas, understand what their needs are and how we can develop this pipeline directed to add value to the business. Having, therefore, a good relationship with all the units involved is crucial for this whole process to take place in a faster, smoother and more efficient way.

3 – Critical thinking and attention to detail.

It is crucial to have critical thinking and attention to detail in the construction of pipelines for data extraction and transformation and load them into databases so that they can be used by other areas. You should be able to look at the problem, break it down into the smallest units possible, and quickly assemble all these pieces so they work more efficiently.

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Personally, I tend to use the following method to build this critical thinking:

  1. Formulate a Hypothesis
    • Get an idea of ​​how to assemble this system
  2. Gather information
    • Bearing in mind my hypothesis, I will collect information about them
  3. Apply this information
    • Check if my hypothesis is feasible
  4. Consider the implications
    • Even if it is feasible, what are the impacts of this solution?
  5. Explore more points of view
    • After we arrive at a possible hypothesis, it is important to test this hypothesis against other points of view, to validate if it is the best possible solution. If not, return to point 1 😊

4 – Never stop learning.

Working in Data Engineering is a challenging job because it gives you the opportunity to have an ever-developing career. We must always bear in mind that staying informed and continuing to learn new technologies is absolutely essential to work in this area. Languages ​​and Framework are constantly evolving, and what is considered standard quickly ceases to be. In this sense, it is essential to always be updated on the new technologies that are at the forefront and, if possible, to join a community where you can have a broader view of how the industry is doing. One that I particularly like and recommend is Towards Data Science.

In conclusion, having the technical knowledge is extremely important, but you need more than that to become an excellent professional. I believe these tips can help you on your way to becoming a better Data Engineer!

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