Worked on the cta-lstchain repository to address a data processing alignment issue in the pedestal and flatfield data pipeline. The solution involved replacing subrun-based indexing with positional indices, using Python and scientific computing techniques to ensure new data was appended correctly even when subrun identifiers were inconsistent. This targeted bug fix improved data integrity and reproducibility by preventing mismatches in data association, which is critical for downstream calibrations. The change was implemented and validated within the standard workflow, enhancing the reliability and auditability of the pipeline. The work demonstrated careful attention to data processing and scientific computing best practices.