Notebooks are essential for the EDA and early prototyping stages but all data scientists should be enough "software engineer" to get their code out of their notebook and into a reusable library/package of tools shared with engineering.
The best teams I've worked on the hand off between DS and engineering is not a notebook, it's a pull request, with code review from engineers. Data scientists must put their models in a standard format in a library used by engineering, they must create their own unit tests, and be subject to the same code review that engineer would. This last step is important: my experience is that many data scientists, especially coming from academic research, are scared of writing real code. However after a few rounds of getting helpful feedback from engineers they quickly realize how to write code much better.
This process is also essential because if you are shipping models to production, you will encounter bugs that require a data scientist to fix that an engineer cannot solve alone. If the data scientists aren't familiar with the model part of the code base this process is a nightmare, as you have to ask them to dust of questionable notebooks from months or years ago.
There are lots of the process of shipping a model to production that data scientists don't need to worry about, but they absolutely should be working as engineers at the final stage of the hand off.