Three reasons to consider becoming a Data Engineer
Science fiction feeds our imagination about the future. As a child, I used to imagine how technology brings us closer to the amazing machines I saw in comics, animations, and movies. It was magical to realize how life imitates art and how many inventions matched what humanity had thought of in the past. I loved Leonardo da Vinci because inventions that had come off the paper at the hands of others were ideated by him long before.
Currently, the technology that generates this feeling in me the most is Artificial Intelligence. Imagining how the machine approaches the human in terms of interaction capacity is something curious and amazing. Without going too much into conceptual issues, it is undeniable that the support of any and all technology that points to AI has one thing in its backstage: DATA! If you want to understand a little about the difference between Data Engineer, Data Scientist, and Data Analyst, I suggest reading this text.
Maybe what motivated me doesn’t spark your interest, but there are plenty of reasons for you to look with interest at the Data field, which grows impressively. According to Statista, “The global big data and business analytics market was valued at 169 billion U.S. dollars in 2018 and is expected to grow to 274 billion U.S. dollars in 2022.”.
If these factors haven’t convinced you yet, here’s a list of reasons for you to consider becoming a Data Engineer.
1 — If you are a programmer, you are already very close to this
Changing fields is not easy. New skills, a lot of information, not knowing where to start… all of these can be limiting factors, but if you are already a programmer, you meet some of the basic requirements for a Data Engineer. In addition, changing fields is not a restart. Your professional experience is very welcome and will help you develop the skills you may not yet have.
Companies in general seek professionals with experience, more seniority. When switching fields, it is important to know that there are foundations of Data Engineering to be learned, that is, one does not start as a senior. However, previous experiences can be a decisive factor in quickly advancing.
2 — Data Engineering is the backbone of the entire Data field.
According to the hierarchy of needs for Data Science proposed by Monica Rogati, Data Engineers are fully responsible for the two bottom items and share responsibility with Data Analysts and Data Scientists for the third bottom item and up.
In practice, companies that are heavily data-driven need a very structured Data Engineering team. As a result, there are often great challenges that can be a huge source of learning due to the need to use advanced technologies. Another factor resulting from this is the next item.
3 — Competitive compensation
The high demand for professionals has generated very competitive compensation compared to other fields.
In the United States, the Data Engineering career is Top Trending, showing an increase of about 88% (year over year).
According to a survey conducted by Stack Overflow in 2021 (values worldwide), the Data Engineer position has on average the fifth highest compensation in the technology field. We know that this varies greatly depending on the country, region, and also seniority, but the potential that the field presents is indeed undeniable.
If this scenario has motivated you, I suggest you start researching a little more about it, seek to learn the foundations and understand what job openings ask for. Bootcamps, quick courses, and meet ups are a good way to take the next step.
