From platform foundations to production data & AI.
I help take enterprise lakehouses from proof of concept to production—setting integration standards, improving Spark performance, and building governed data and AI workflows.
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 reduction and records processed are approximate professional outcomes, separate from the ongoing CRM migration. Volume and dataset counts describe platform scale, not sole individual accomplishment.
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.