The buzz around data scientist jobs 10 years ago was undeniable — the position was even called the sexiest job of the 21st century by Harvard Business Review. Now hype around the career has settled. The need for data scientists hasn’t disappeared, but it’s no longer a scramble to fill early-career roles.
If you’re interested in landing a job in data land and have some programming skills, aim for a career as a data engineer.
There are four market-driven reasons that shooting for a data engineering position is your best bet for landing a good-paying data job:
1. Supply and demand
Competition for entry-level data scientist positions is extremely stiff. Data scientist roles see far more applicants per opening than data engineer roles. One 2020 study of LinkedIn job listings found nearly twice as many applicants for data scientist listings, on average.
It’s not surprising that data science openings receive more applications. Nearly every degree-granting institution seems to have developed undergraduate and graduate programs in data science. A proliferation of data science specialties in universities and bootcamps has equipped thousands of data scientist job seekers.
In contrast, there are zero data engineering bachelor and master’s degree programs in the US. Live immersive remote or in-person data engineering bootcamps are likewise completely missing.
Meanwhile, demand for data engineers has grown. As data proliferates, organizations need more data engineers to ensure it goes where it should, in the forms that are most useful for other data professionals and end users.
The Dice Tech Job Report for the first half of 2022 showed data engineers were the fourth most listed tech occupation. Data scientists were eighth. While data scientist positions saw 68% year-over-year growth — which is nothing to sneeze at — data engineer positions saw 100% growth!
Pair this demand for data engineers with the lack of formal training paths and it’s no surprise salaries are strong. Basic labor economics says that salaries will rise when demand exceeds supply.
Salaries, although hard to pin down definitively, appear to be higher for data engineers relative to data scientists. Indeed reports the average base salary for a data scientist is a very respectable $102K. Nonetheless the average base salary for a data engineer is nearly 15% higher, over $115K.
3. Startup opportunities: No data, no data scientist
It might sound obvious, but you need data if you want a data scientist to be able to build a machine learning model, do analysis, or create a dashboard. Unless a startup is focused on machine learning, it will need a data engineer to capture, transform, and move that data before it needs a data scientist.
AngelList, a popular site for startup jobs, showed 15% more open data engineer positions than data scientist positions. If you want to be a data professional with an early-stage, high-growth startup, data engineer is your best bet.
4. Getting into data engineering
The path to a data engineer role is less clearly defined than for many careers. This creates both an opportunity and a challenge for job seekers. It’s easier to stand out, but you will need to chart your own course.
The bottom line is that SQL and Python are must haves. In my more recent unpublished research, cloud computing skills with AWS, Microsoft Azure, or Google are increasingly popular. And in today’s cloud native landscape, some familiarity with Prefect or Airflow, dbt, and Airbyte will set you apart.
A computer science or data science degree or bootcamp, paired with projects demonstrating your skills in the above areas, will make you an excellent candidate for entry-level data engineering jobs.
If you’re looking to break into high-paying data careers, set your sights on data engineering.