What are some common feature engineering techniques to improve model performance?

Feature engineering seems to play a big role in improving model accuracy, but what are the most common techniques you use? Whether it’s scaling, encoding categorical variables, or creating new features, I’d love to hear which approaches have worked for you. Also, how important is domain knowledge in feature selection, especially for complex datasets? Do you rely more on domain experts or use automated methods like feature importance?