Worked on enhancing data quality reporting within the Nike-Inc/spark-expectations repository by implementing a feature that populated source_query_dq_results for all rules, providing comprehensive visibility into data quality outcomes. Refactored the rule results handling to include status information, which improved the accuracy and governance of data quality reporting. Leveraged Python and Spark to streamline the filtering of non-passing rule statuses, reducing noise and enabling faster remediation workflows. Focused on ETL and data quality best practices, the work addressed the need for more reliable and actionable reporting, resulting in a more maintainable and effective data quality monitoring process for the project.