Sr. Specialist Solutions Architect — Healthcare & Life Sciences | Databricks
I’m a Sr. Specialist Solutions Architect at Databricks, focused on Healthcare & Life Sciences, with deep specialization in Data Warehousing and Data Engineering. With over 25 years of experience in software development—and more than a decade dedicated to large-scale data systems—I help organizations design and implement production-grade lakehouse architectures that unify analytics, governance, and AI on a single platform.
My work centers on helping customers modernize legacy data platforms by adopting the Databricks Data Intelligence Platform—leveraging Lakeflow for end-to-end data engineering, Unity Catalog for unified governance, and Serverless SQL Warehouses for high-performance analytics. I specialize in designing scalable Medallion architectures, implementing real-time and batch pipelines, and enabling teams to move from fragmented toolchains to a cohesive, governed data ecosystem.
Prior to Databricks, I spent nearly a decade architecting data platforms in healthcare and energy — as Principal Big Data Architect at CodaMetrix (building their Lakehouse Intelligent Platform on Databricks and Kafka), Principal Data Engineer at HealthVerity (leading a full platform re-architecture from legacy EMR to Databricks), and across five years at Enverus designing data lake and analytics solutions for the Oil & Gas industry using Spark, Delta, and Azure. Before pivoting to data, I spent 10+ years as a software engineer and architect building high-scale web platforms at companies like SocketLabs and Comcast.
🎤 DATA+AI Summit Talk: Building Scalable Data Platforms with Medallion Architecture (YouTube)
- Languages: Scala, Python, SQL
- Data Engineering: Lakeflow (Declarative Pipelines, Connect, Jobs), Medallion Architecture, Batch & Streaming Pipelines, Auto Loader
- Data Warehousing: Serverless SQL Warehouses, Lakehouse Federation, Liquid Clustering, Predictive Optimization
- Governance & Security: Unity Catalog, ABAC, OpenSharing, Data Lineage, Data Classification
- Platform: Apache Spark, Delta Lake, Delta UniForm, Managed Iceberg Tables
- AI for Data Engineering: AI Functions (ai_extract, ai_classify, ai_query), Genie Spaces
- CI/CD & Automation: Databricks Asset Bundles, Lakeflow Jobs, Databricks Apps
- Platform Modernization: Migrating legacy on-prem and cloud data warehouses to the Databricks Lakehouse
- Lakeflow Adoption: End-to-end managed data engineering with Lakeflow Connect (300+ connectors), Declarative Pipelines, and Jobs orchestration
- Governed Analytics: Designing Unity Catalog-driven governance with attribute-based access control, lineage tracking, and cross-platform data sharing via OpenSharing
- Performance at Scale: Liquid Clustering, Predictive Optimization, and Serverless compute for cost-effective, high-throughput workloads