Welcome to your interview.
The question is on your right: Feature Engineering for New Models. Take a moment with it first.
Talk your thinking through with me if you like - when you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes). Discussion and graded submissions share your five interviewer interactions, so spend them well.
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?