Built an end-to-end data pipeline leveraging Docker, Airflow, DBT, and SQL databases to automate data ingestion, transformation, and storage. The pipeline ensures scalability, consistency, and persistence, with containerized services and modular data transformations for real-world applications. Automated workflows using Apache Airflow to orchestrate tasks and Kafka for real-time data streaming. Integrated cron jobs and Docker volumes for seamless scheduling and data persistence, ensuring timely and reliable updates for analytics and reporting.