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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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?
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 waysThe 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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It’s hard to develop significant expertise in being a data engineer, data analyst and data scientist when you’re trying to learn all of them at once
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100%! my point is that it is hard because all current infra is waaaay too complex and with better infra I would hope roles would be more blurry and collapse. Until then I totally understand why we have such fragmentation
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Idk I’m always skeptical of being oriented around tools rather than the larger business goal. Not every goal calls for every tool, so having flexibility to use whatever is the right tool for the job seems good
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Oh I definitely see this as being oriented around business goals, but more able trying to solve different dimensions of them. Anyone can use any tool that’s relevant, but some folks might focus on getting quick answers, some on precise answers, some on scaled answers
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