Data Scientists versus Data Engineers

By Lillian Pierson

The roles of data scientist and data engineer are frequently completely confused and intertwined by hiring managers. If you look around at most position descriptions for companies that are hiring, they often mismatch the titles and roles, or simply expect applicants to do both data science and data engineering.

If you’re hiring someone to help you make sense of your data, be sure to define your requirements very clearly before writing the position description. Since a data scientist must also have subject matter expertise in the particular area in which they work, this requirement generally precludes a data scientist from also having expertise in data engineering (although some data scientists do have experience using engineering data platforms).

And if you hire a data engineer who has data-science skills, he or she generally won’t have much subject-matter expertise outside of the data domain. Be prepared to call in a subject-matter expert to help him or her.

Because so many organizations combine and confuse roles in their data ­projects, data scientists are sometime stuck spending a lot of time learning to do the job of a data engineer, and vice versa. To get the highest-quality work product in the least amount of time, hire a data engineer to process your data and a data scientist to make sense of it for you.

Lastly, keep in mind that data engineers and data scientists are just two small roles within a larger organizational structure. Managers, middle-level employees, and organizational leaders also play a huge part in the success of any data-driven initiative. The primary benefit of incorporating data science and data engineering into your projects is to leverage your external and internal data to strengthen your organization’s decision-support capabilities.