Developed a floating-point range validation enhancement for the databrickslabs/dqx repository, extending the is_in_range and is_not_in_range utilities to accurately handle floating-point values. This update addressed a data-quality risk by enabling precise validation of float ranges within analytics pipelines. The work involved implementing comprehensive unit and integration tests using PySpark and Python to ensure correctness across a variety of scenarios, complemented by manual validation for thorough coverage. The solution was delivered with minimal performance impact and improved maintainability, resulting in more reliable data validation utilities that enhance data quality and support robust analytics workflows across downstream systems.