Enterprise ETL & Data Warehouse Platform

Designed and developed a scalable enterprise ETL and data warehousing platform for consolidating large volumes of structured and semi-structured business data into a centralized analytics ecosystem. The client required a high-performance reporting infrastructure capable of integrating data from multiple operational systems while improving reporting speed, data quality, and business visibility. I designed automated ETL workflows using Python and Apache Airflow, implemented optimized relational database models, and developed Power BI dashboards for executive-level KPI monitoring. Major responsibilities included database schema design, SQL performance optimization, API integrations, incremental data loading strategies, data transformation workflows, and automated reporting pipelines. One of the main challenges involved handling inconsistent legacy data structures and improving performance for high-volume analytics queries. I solved this by redesigning indexing strategies, implementing partitioning techniques, and optimizing query execution plans. Results: • Reduced reporting time by more than 70% • Improved data consistency across departments • Automated manual reporting workflows • Increased scalability and maintainability of the data platform