I think this specialization of data teams into 99 different roles (data scientist, data engineer, analytics engineer, ML engineer etc) is generally a bad thing driven by the fact that tools are bad and too hard to use
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That’s is true but seems like a related but different problem? Even companies with super clear expectations on data teams seem to go in the direction of fragmentation, I think
I guess I did get a little sidetracked from your original point 
But IMO fragmentation can be a good thing if it’s about clarifying responsibilities. Analysts and scientists may have overlapping skills, but focus their effort on creating value from data in different ways
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I agree tooling is often a red herring, but I think specialization within the DS world can be a positive thing
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Maybe stated differently: fragmentation of titles created because tools suck is a bad thing, but fragmentation of titles to align people with constrained (and thus more tractable) problems to solve seems like a good thing.
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Sure but just digging a bit deeper, what’s the benefit of that specialization? If both roles could do both things (I’m not saying they can now, because tools get in the way imo), wouldn’t that be better?
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The boundary between different DS roles is pretty thin, and I think that’s good. However, I see a lot of benefit in only trying to apply one type of problem solving lens at a time. Job title is a way to formalize that for longer periods, but it could be a project by project thing
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