Parselva developed a Code Quality Metrics feature for the IBM/data-prep-kit repository, focusing on enhancing data preparation workflows through improved analytics. Leveraging Python and data engineering skills, Parselva implemented metrics for word frequency, alphabetic character percentage, and encoded data statistics, providing deeper visibility into data quality. The solution integrated seamlessly with existing modules, maintaining performance while supporting early issue detection and governance requirements. Parselva ensured traceability by linking the work to a specific commit, reflecting a disciplined approach to code analysis. This contribution addressed the need for proactive quality improvements in data pipelines, demonstrating technical depth within a short timeframe.