Worked on the Nike-Inc/spark-expectations repository to enhance the configurability of error table naming within the SparkExpectations context. Developed a feature in Python and Spark that enables users to specify custom error table names, with a default fallback to the <target_table>_error convention when no name is provided. This approach helps maintain consistent naming standards and reduces the risk of table-name collisions in downstream data pipelines. The implementation included comprehensive unit testing to validate both user-defined and default behaviors, as well as updated documentation to guide users in adopting the new configuration option for improved data quality management workflows.