At Johnson & Johnson, I lead a nine-member data engineering team delivering cloud data products for pharmaceutical sales, market insights, customer engagement, and executive reporting. I build hybrid AWS and Azure platforms using Databricks, Delta Lake, Snowflake, Python, SQL, and PySpark.
I've improved pipeline throughput by 45%, reduced reporting discrepancies by 60%, and lowered annual compute costs by approximately $500K through Spark, Databricks, Snowflake, and SQL optimization. I also orchestrate 800+ workflows, modernize legacy ETL, and establish data quality, governance, observability, and privacy controls.
Previously, I built portfolio and risk data pipelines at Black Rock and healthcare reporting pipelines at Mayo Clinic. I bring hands-on experience across AWS, Azure, GCP, streaming, lakehouse architecture, dbt, Terraform, and AI-enabled data engineering.
