Designing enterprise data platforms from ingestion to governed analytics and AI.
At Veeva, I help evolve a Databricks lakehouse from proof of concept to production through integration standards, Spark performance work, and shared governance patterns.
Historical records processed in a major ingestion initiative.
100+ TB
Lakehouse volume
Platform scale: the production Databricks lakehouse I helped evolve.
120+
Datasets
Platform scale: datasets across the enterprise lakehouse.
Cost and ingestion figures are approximate professional outcomes, separate from the ongoing CRM migration. Platform volume and dataset counts describe shared systems I helped engineer, not individual output.
A shared Databricks foundation for operational and analytical data.
PLATFORM ARCHITECTURE & ENGINEERING
LAKEHOUSE ARCHITECTURE
Engineering standards across the platform.
I helped evolve a Databricks lakehouse from proof of concept to production, defining ingestion, modeling, and governance standards used by engineering and analytics teams.
Batch and near-real-time integration reference patterns
Bronze/silver/gold modeling with Delta Lake and dbt
Unity Catalog, data contracts, lineage, and access control
Spark performance work, architecture reviews, and mentoring
How DataNepal separates source handling, analytical modeling, and catalog-validated exports—and the tradeoff behind static delivery.
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CURRENT ROLE
Senior Data Engineer
Veeva Systems · January 2025–Present
At Veeva since January 2022; promoted to Senior Data Engineer in January 2025. Lead architecture reviews and mentor engineers on Spark optimization, modeling, and production patterns.