Not to disparage the amazing data scientists I've worked with, but I've been on teams where this is very much the approach to operationalizing models. It's basically, "Here's the sklearn model and some fragile featurization scripts we built. Can you take this to prod ASAP?"

The problem I've seen is that DS & DE teams were in different parts of the org and had their own sprints that were in no way connected. So they kept chucking models over the wall and we kept trying to faithfully operationalize. Once we convinced leadership that we had to collaborate from the get-go, things went a whole lot better. It also improved the working relationship of engineers and scientists.

I learned a hell of a lot from the scientists; they learned how to write better code. They also learned what code they didn't need to write because I could do it faster or better than them, leaving them to focus on more important things. It was pretty amazing to find what manual processes they would setup in lieu of proper (or even any) engineering support. Again, these are amazingly smart people, but they were being square-pegged into a lot of round-hole engineering tasks.

Now, the much more frustrating issue I had was being in a very data-heavy organization and being told by a distinguished engineer (my skip-level) plus my direct manager that, "data engineering isn't a real discipline." I left that org very shortly thereafter.