Engineered end-to-end Wealth Analytics platform on Azure (ADF, Databricks, ADLS Gen2) enabling real-time client portfolio intelligence for 19K+ financial advisors. Built AI-driven segmentation and risk modeling pipelines (PySpark, MLflow) improving retention predictions by 21% and investment recommendations by 18%. Designed ETL/ELT pipelines processing 150+ TB/day from SQL, Bloomberg API, and Refinitiv into governed Delta Lake for Power BI analytics. Implemented governance and observability with Unity Catalog, Purview, and Great Expectations ensuring data lineage, PII masking, and regulatory compliance. Modernized reporting and regulatory workflows under Azure Synapse reducing manual audit prep by 70%. Productionized AI forecasting models via Azure ML, boosting AUM forecast accuracy by 25%.

Data Engineer at OPTUM

December 1, 2022 - August 1, 2024

Designed and deployed 15+ scalable data pipelines on AWS Glue and Lambda processing 300M+ daily patient records for real-time analytics. Optimized Snowflake warehouse with partitioning/clustering, reducing query runtime by 48% and cloud costs by $12K/month. Migrated legacy on-prem Oracle DB claims system to AWS S3 and Snowflake with AWS Athena for ad-hoc analytics. Implemented incremental loads via Lambda reducing runtimes from 8 hours to 90 minutes. Revamped Tableau dashboards by restructuring SQL and aggregations in Snowflake, improving refresh speeds by 60%.

Data Engineer at Hub Group

January 1, 2020 - August 1, 2022

Created 20+ data pipelines processing 500M+ records per day using ADLS Gen2, Azure Data Factory, Azure Databricks, Azure Synapse, Python, and Spark to drive supply chain insights. Diagnosed and resolved bottlenecks in Databricks notebooks, reducing ETL time by 63%. Improved Power BI data refresh by changing the load type of fact tables in SQL Server data warehouse, boosting refresh by 57% and user adoption by 20%.