[NumberCruncher]

The "the right tool for the right job" applies for ML topics too.

If the job involves "looking smart and innovative" for whatever reasons, people tend to err on the side of overly complex solutions.

On the other hand if the advice "let's just go with an SQL query built on a stack of egregiously oversimplifying assumptions" comes from someone, who doesn't know how SQL and linear regression / logistic regression with binning/bucketing / simple decision trees work, I would ask for a second opinion. Because a huge part of the retail banking, non-life insurance and marketing business is running on this simple stack. Obviously profitable.

If the same advice comes from someone, who knows when to use deep learning instead of XGBoost and why, I would go with his/her advice. And I would try to keep him happy and on my team.