Your question is Feature Engineering for Tabular Models. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
You are working on a supervised learning problem and need to improve model quality beyond basic raw inputs. You want a structured way to create, validate, and ship features without overfitting or introducing leakage.
How do you approach feature engineering in a machine learning project?