When handling missing data during feature engineering, you can remove instances with missing values, fill in missing values with mean/median/mode, or use more advanced techniques like imputation methods (e.g., K-nearest neighbors or regression imputation). For outliers, you can consider removing them if they are shown to be incorrect or transform them using techniques like winsorization or capping.