While most machine learning tutorials and challenges focus on single-table datasets, real-world enterprise data is often distributed across multiple tables, such as customer logs, transaction records, or manufacturing logs. In this talk, we address the often-overlooked challenge of building predictive features directly from raw, multi-table data. You will learn how to automate feature engineering using a scalable, supervised, and overfit-resistant approach, grounded in information theory and available as a Python open-source library. The talk is aimed at data scientists and ML engineers working with structured data; basic machine learning knowledge is sufficient to follow.