Worked on the OpenSTEF/openstef repository to enhance the robustness and adaptability of forecasting data workflows. Addressed a critical bug by ensuring fallback generation correctly uses the forecast’s time-based index during data merges, preventing errors from referencing non-existent columns. Refactored metric calculation logic to dynamically utilize the input pandas Series name, allowing seamless support for varying data series without code changes. These updates improved data integrity, reduced merge errors, and increased maintainability for future development. The work relied on backend development and data analysis skills, leveraging Python and the Pandas library to deliver more flexible and reliable data processing pipelines.