Developed a Python dedent utility for the smart-data-lake/smart-data-lake repository, addressing the challenge of handling multiline Python strings within Spark transformations. The solution removes common leading whitespace, ensuring that indented Python code blocks execute correctly and reducing the risk of runtime errors caused by misaligned code. The work included comprehensive unit tests to validate the dedent behavior, demonstrating a test-driven approach and attention to code reliability. Leveraging Python, Spark, and Scala, the developer improved the reliability and maintainability of Spark data pipelines while enhancing developer productivity through clearer string handling and expanded test coverage within the project.