The art and science of converting domain data (timestamps, text, sensor readings) into meaningful inputs - creating polynomial features, encoding categorical variables, constructing interaction terms, or normalizing distributions. Good feature engineering reduces model complexity, enhances interpretability, and can embed domain knowledge. Governance needs to track feature lineage, validate transformations, and assess feature-drift impacts on model fairness.