> Yeah, as a "T-shaded data folk" (have worked as a data analyst, data scientist, data engineer, and machine learning engineer, so I think I'm close to a full bingo care) in data, I agree!

Me too. There were no data engineers when I was a baby data scientist (15 years ago now).

I think that this was a terrible idea (the splitting out of roles). I remember once being asked in an interview if I was able to pull my own SQL or would I need an analyst. That kind of split always struck me as absurd, given that the only way to build a decent model is to know the data very well first..

> That said, I think we have a culture problem in defining our roles narrowly, I've seen data scientists look down at data-pipeline parts of their job, and ignoring model latency and deployment; data engineers be deeply uncurious around what happens to the data after they put it in a warehouse; etc.

I honestly feel like this is driven by the titles. I will note that most of the unsuccessful/bad data scientists I've met were the ones who only cared about the models. As I always said to people, I can run sklearn in a loop over model families if that's all you bring.

> The best people I've worked with, within data and elsewhere, are the ones who are curious about every aspect of the stack, even if they mostly focus on one side.

This is probably just a good signal in tech in general, some curiosity is really required to be successful in this industry.