Developed an automated dataset validation feature for the d-fine/DatalandQALab repository, focusing on reducing manual quality assurance effort and improving data reliability. The solution introduced a server-side scheduled processing function in Python, enabling both continuous and single-pass validation modes to accommodate different review workflows. Dependencies were updated to support the new functionality, and comprehensive end-to-end tests were expanded to ensure robust coverage of the validation process. By integrating automation, backend development, and testing skills, this work streamlined the data QA pipeline, allowing for scalable and flexible dataset checks while maintaining high standards of data quality within the project.