The buzz around data scientist jobs 10 years ago was undeniable – the position was even named the sexiest job of the 21st century by Harvard Business Review. Now the hype surrounding the quarry has settled down. The need for data scientists hasn’t gone away, but early-career positions are no longer difficult to land.

If you’re interested in finding a job in data and have some programming skills, consider a career as a data engineer.

There are four market-driven reasons why looking for a data engineering position is your best bet for finding a well-paying data job:

1. Demand and supply

Competition for entry-level data scientist positions is extremely strong. Data scientist roles see far more applicants for a vacancy than data engineer roles. one 2020 LinkedIn Job Posting Survey found on average almost twice as many candidates for data specialist listings.

Not surprisingly, data science jobs receive more applications. Almost every degree-granting institution seems to have developed undergraduate and graduate programs in data science. The proliferation of data science majors in universities and bootcamps has prepared thousands of job-seeking data scientists.

In contrast, there are no bachelor’s and master’s programs in data engineering in the US. There is also a complete lack of engaging remote or in-person data engineering bootcamps.

Meanwhile, the demand for data engineers is growing. As data proliferates, organizations need more data engineers to ensure it goes where it needs to go, in the forms that are most useful to other data professionals and end users.

The Dice Tech Performance Report for the first half of 2022 showed that data engineers were the fourth most-listed technology occupation. Data scientists are eighth. While data scientist positions saw a 68% year-over-year growth – which is nothing to sneeze at – data engineer positions saw 100% growth!

Combine this demand for data engineers with the lack of formal training paths, and it’s no surprise that salaries are high. Basic labor economics says that wages will rise when demand exceeds supply.

2. Salaries

Salaries, while difficult to determine definitively, appear to be higher for data engineers compared to data scientists. Indeed, the average base salary for a data scientist is reported to be very respectable 102 thousand dollars. However, the average base salary for a data engineer is nearly 15% higher 115 thousand dollars.

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, run an 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 startup job site, showed 15% more open data engineer positions than data scientist positions. If you want to be a data professional with a high-growth early-stage startup, a data engineer is your best bet.

4. Getting into data engineering

The path to a data engineer role is less clearly defined than many careers. This creates both an opportunity and a challenge for job seekers. It’s easier to stand out, but you’ll have to chart your own course.

me before analyzes the technologies employers are looking for in data engineers.

The bottom line is that SQL and Python are a must. In my more recent unpublished research, cloud computing skills with AWS, Microsoft Azure or Google are becoming increasingly popular. And in today’s native cloud landscape, some knowledge of Prefect or Airflow, dbt and Airbyte will set you apart.

A computer science or data science degree or boot camp combined with projects demonstrating your skills in the above areas will make you an excellent candidate for entry-level data engineering jobs.

If you want to break into high-paying data careers, set your sights on data engineering.

https://www.informationweek.com/big-data/want-a-data-job-learn-data-engineering-not-data-science

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