Worked on the IBM/data-prep-kit repository to enhance the robustness of the Code Quality Transformation Module, focusing on data engineering and error handling using Python. Addressed a schema exception related to the top_two_words_percent metric by implementing stricter data-type validation and introduced logic to handle empty files, thereby preventing processing errors and ensuring accurate metric calculations. Consolidated these improvements into a single squash commit to maintain clear project history and facilitate traceability. The work strengthened the reliability of the data pipeline, reduced downstream failures, and improved the accuracy and trustworthiness of analytics dashboards through careful code quality analysis.