During April 2025, Dug Word enhanced data quality reporting for the Nike-Inc/spark-expectations repository by implementing a feature that populates source_query_dq_results for all rules, enabling comprehensive visibility into data quality outcomes. Using Python and Spark, Dug refactored the rule results handling to include status information, which improved the accuracy and governance of data quality reporting. The work also introduced more effective filtering of non-passing rule statuses across data quality checks, reducing noise and streamlining remediation workflows. This focused engineering effort addressed a specific reporting challenge, demonstrating depth in data quality and ETL processes within a modern data engineering context.