During March 2026, Andrey Alopatin enhanced Apache Airflow’s reliability in managing Spark jobs by developing an automatic timeout-based deletion mechanism. He implemented logic in Python to detect Spark jobs that fail to start within a configurable period and ensure their removal, preventing orphaned resources and improving error handling. The solution included comprehensive unit testing to verify correct deletion behavior and a targeted variable rename to clarify code intent. Andrey’s work focused on backend development and robust error handling, addressing a specific operational gap in Spark job management and contributing to more predictable and maintainable workflows within the Airflow repository.