Good overall post but there is some conflation with machine learning. The issue with conflating the two is that, in practice, many engineers want to do ML work but few want to perform analysis work.

One additional factor for success:

Hire with the right expectations. If most of the value from the role comes from data analysis, communicate that. Often ML/data roles are hired by dangling the carrot of developing and deploying complex machine learning products. ML is sexy, a lot of managers want to manage ML projects and a lot of engineers want to work on them. In reality, most teams need someone that is good at SQL and can code a simple metric/heuristic. It’s also important to communicate to the team that shipping simple solutions and simple analysis is a great outcome. Your analysis showed that you can achieve 90% of the initial goal with a simple if/else on one metric? Great! Deploying and maintaining ML models is hard and should be a last resort. I’m saying that as someone whose entire career depends on complex machine learning models.