Ahmed Samy Elgendy developed automation enhancements for the omar-gamal99/talabat_bootcamp repository, focusing on Airflow-based data pipeline reliability and scalability. He implemented a daily ETL DAG using Python and YAML, orchestrating the my_etl_function to ensure consistent data extraction and transformation. Ahmed introduced a master trigger DAG to centralize manual triggering of core extract processes, reducing operational overhead. He also enabled dynamic DAG creation from YAML configurations, streamlining provisioning across environments. By fixing ETL DAG start date formatting, he improved scheduling accuracy. The work demonstrated solid data engineering and DevOps skills, delivering three features and one bug fix within a month.