[anonytrary]

I define data engineering as something like "implementing ML algorithms on servers for real-world use cases", in which case they're mostly just gluing together function calls that other people figured out. "Data science" on the other hand is the stuff that actually requires using statistics and math to figure out what operations are necessary on a data set.

Plus, I could say the same thing about ML. It's just graph theory, linear algebra and calculus with some statistics mixed in. Where's the absolutely necessary programming? There are plenty of opportunities to do ML theory with little programming, if any. There absolutely needs to be a distinction between theorists and engineers, because they aren't the same thing. Most of the programming is the grunt work you pass off to the engineers.