1 - Data engineering and DevOps
2 - It's way less stressful than ML because you have really clear requirements ( I need to get data from a source in a certain target with those constraints ). This sometimes can be challenging due to business requirements (Time, consistency, and monitoring those pipelines) but I find it better than go into a project where I don't even know if it will be feasible or no.
3 - I was a good programmer before I got to ML, so for me it was like I switched back to what I used to do, so it was not a big deal. ( My curriculum was a lot of software engineering / managing networks and pure dev)