During May 2025, contributed to the IBM/data-prep-kit repository by developing a Code Quality Metrics feature aimed at enhancing data preparation workflows. This work introduced analytics for word frequency, alphabetic character percentage, and encoded data statistics, providing deeper visibility into data quality and supporting governance across pipelines. Leveraging Python and data engineering skills, the implementation focused on seamless integration with existing modules while maintaining performance. The feature’s traceable commit ensured accountability and reproducibility within the project. By enabling early detection of quality issues, this contribution strengthened the foundation for proactive improvements in data analysis and preparation using code analysis techniques.