From proof of concept to production
At Veeva Systems, I served as a technical architect for an enterprise data platform that grew from an initial proof of concept to a production Databricks lakehouse with 100+ TB and 120+ datasets.
The work included defining ingestion, modeling, and governance standards adopted across engineering and analytics teams. It supports both operational and analytical use cases.
Integration patterns and modeling
My scope includes end-to-end architecture for batch and near-real-time integrations across SaaS applications, REST APIs, AWS Kinesis, and SFTP. I established a bronze/silver/gold medallion reference design used by downstream teams.
This work connects ingestion and distributed processing with the data models consumed by reporting and analytics. Python, SQL, PySpark, Delta Lake, and dbt are part of that engineering scope.
Performance, governance, and reliability
I reworked high-cost Databricks workloads using Photon, incremental processing, partition pruning, and Spark tuning. Across my professional work, the approved approximate reduction in Spark-related processing cost is 35%.
Governance and reliability work spans Unity Catalog, data contracts, lineage, role-based access control, and automated data quality. These are platform responsibilities alongside pipeline implementation.
Technical leadership
I lead architecture reviews and mentor engineers on Spark optimization, data modeling, and production design patterns. I also partner with analytics and business teams on technical solution design and delivery.
Scope of this overview
This overview draws from my resume and public LinkedIn profile. Scale figures describe the platform; they do not imply that I built every dataset or worked alone. Internal schemas, infrastructure configuration, business rules, and confidential implementation details are omitted.
The ongoing CRM migration is a separate initiative. Its scope does not imply a completed cutover.