Developed a multi-processing performance enhancement for the header cleansing workflow in the IBM/data-prep-kit repository, focusing on scalable data processing and robust system administration. The work introduced configurable parallelism, allowing users to specify the number of processes, temporary directory locations, and timeout settings for header cleansing tasks. Implemented in Python, the solution incorporated advanced code analysis and parallel computing techniques to improve throughput and reliability. Enhanced error handling and timeout logic were added to reduce failure modes in long-running operations. Documentation and dependencies were updated to support these new capabilities, resulting in a more efficient and configurable data preparation pipeline.