Feature engineering uses domain knowledge to create, transform, or select input variables that make ML models work more effectively. Examples include converting timestamps into day-of-week features, combining address fields into distance metrics, or creating financial ratios. While deep learning learns features automatically, feature engineering remains essential for tabular business data and often delivers bigger gains than model tuning.