Worked on the Subaru-PFS/datamodel repository to enhance the reliability of the pfsZCandidates data model by improving its error handling mechanisms. Introduced a new NO_ERROR code to the ZLError enumeration, addressing a missing error state and enabling more precise diagnostics for downstream data workflows. The implementation focused on robust Python programming and careful data modeling, ensuring that the changes maintained backward compatibility and did not disrupt existing processes. Emphasized error handling best practices throughout the update, validating the solution through targeted code review. This work strengthened the foundation for future development and improved the maintainability of critical data pipelines.