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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I agree tooling is often a red herring, but I think specialization within the DS world can be a positive thing
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 waysMaybe 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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I think the counterpoint is that it can be bad if it pushes people further away from thinking about business outcomes as the ultimate goal. But yeah there’s some optimal level of specialization, I just get the feeling the vastness of tools and complexity pushes that level
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Yeah, like Kubernetes. That tool requires a specialization all to itself. When a tool demands a specialization rather than the other way around, Doesn’t feel right. We are often told ML is a superset of engineering, which laden with its own complexity to begin with
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