To address growing data volume and cost concerns, we focused on accelerating ETL pipelines using serverless architectures that eliminate the need for dedicated infrastructure. Optimizing BigQuery with partitioning and clustering strategies minimized compute and storage expenses. Automation of repetitive data tasks streamlined workflows, reducing manual effort and error risk. This approach ensures scalable, cost-effective data processing, enabling faster insights and better resource utilization to support business growth and agility.