Decoding the Roles in Data and ML Teams
As an instructor for data professionals, I often find myself in the awkward position of being asked questions that don’t have a clear answer. For example, an aspiring data scientist once asked me, “What’s the difference in day-to-day tasks between a Data Engineer and a Data Scientist, or between a Data Scientist and an ML Engineer?”
It feels strange when I can’t give a simple, concise answer to such straightforward questions. The truth is, there’s so much context involved that it’s nearly impossible to provide a direct answer without oversimplifying.
That’s why I decided to write this blog post—to shed light on the nuances of different roles within data teams and provide the context needed to make the answers to these kinds of questions clear and intuitive.
Let’s get one thing out of the way: in my opinion, roles should be well-defined and agreed upon within a team, even if people end up wearing multiple hats. Roles aren’t restrictions; they’re just a way to group tasks and responsibilities. No one has to fit perfectly into one box. Someone might do both Data Engineering and Data Science tasks—and that’s fine.
