- Handling missing or inconsistent columns in source tables
- Converting date and numeric values safely
- Aggregating team and program metrics efficiently
- Applying ETL best practices in Python
- Working with SQLAlchemy and pyodbc for database connections
- Logging ETL process and error handling
- Automated visual reports (charts/graphs)
- Integration with Generative AI for report summaries
- Scheduling ETL pipeline for automated runs
- Enhanced logging with notifications