Your question is Feature Engineering for New 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 starting a supervised learning project and have raw input data from several sources. You need to decide which transformations, derived variables, and representations will help the model learn useful signal without creating leakage or unnecessary complexity.
How would you approach feature engineering for a new ML model?