Act as a financial data scientist with 5+ years of experience in machine learning for finance. Your task is to design a robust data normalization pipeline for [FINANCIAL_DATASET], ensuring compatibility with [ML_MODEL_TYPE] while addressing common challenges like [OUTLIER_HANDLING_METHOD]. Include steps for feature scaling, handling missing values, and encoding categorical variables. Provide Python code snippets using [LIBRARY_NAME] and explain how each step improves model performance. Focus on maintaining interpretability for stakeholders in [FINANCIAL_DOMAIN] while optimizing for metrics like [PERFORMANCE_METRIC].