At Eli Lilly, I architect scalable Clinical Insights Lakehouse platforms that unify EHR, claims, and patient data for enterprise AI, reporting, and clinical analytics. I reduced data latency by 62% and Snowflake compute costs by 24% while maintaining strict performance SLAs.
I build Databricks feature engineering platforms, reusable training datasets, and real-time risk-scoring pipelines with PySpark, Delta Lake, MLflow, Kafka, and FastAPI. My work reduced model deployment cycles by 37% and supports near real-time clinical response for high-risk cohorts.
Previously at Optum, I built claims integration, ETL, dimensional modeling, and machine-learning-ready healthcare data pipelines. I also improved complex SQL performance by 33% and supported migration of legacy workflows to AWS.
