From POC to production
Technical architecture for a Databricks lakehouse with 100+ TB and 120+ datasets. Defined ingestion, modeling, and governance standards used by engineering and analytics teams.
EXPERIENCE
Building the platform.
Setting the standards.
At Veeva, I have helped evolve an enterprise data platform from proof of concept to a production Databricks lakehouse—then extended that foundation into enterprise integrations and applied AI.
Senior Data Engineer
January 2025–Present
I design and evolve enterprise data platforms—from lakehouse architecture and integration standards to governed analytics and applied AI. At Veeva, my work has helped take a Databricks platform from proof of concept to production.
Integration reference design
End-to-end architecture for batch and near-real-time integrations across SaaS, REST APIs, AWS Kinesis, and SFTP, using a bronze/silver/gold medallion design.
Performance & trust
Photon, incremental processing, partition pruning, and Spark tuning; governance and reliability patterns using Unity Catalog, data contracts, lineage, RBAC, and automated data quality.
Applied AI & technical leadership
LLM/RAG workflows for document processing, structured extraction, metadata enrichment, and vector search. Lead architecture reviews and mentor engineers on Spark, data modeling, and production design.
CURRENT FLAGSHIP INITIATIVE
Enterprise CRM migration
I am the technical owner for the data engineering side of an ongoing enterprise CRM migration. My scope includes migration, source-to-target mapping, reconciliation and data quality, integration architecture, and ingestion from the new CRM.
It also includes operational and analytical reporting, downstream modeling, permissions and governance, and coordination with business and technical stakeholders.
View the ownership overviewPROFESSIONAL IMPACT
Engineering outcomes and platform scale
≈35%
Lower processing cost
Reduction in Spark-related processing cost.
≈39.6M
Historical records
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 historical ingestion are approximate outcomes across my professional work, separate from the ongoing CRM migration. Volume and dataset counts describe platform scale, not sole individual accomplishment.
CAREER PROGRESSION
Earlier experience
Data Engineer
- Built and operated production batch and streaming pipelines with Databricks, Spark, Delta Lake, AWS Kinesis, and REST APIs.
- Standardized orchestration, CI/CD, testing, monitoring, and alerting with Apache Airflow, Databricks Jobs, and Git. Developed ETL/ELT models in PySpark, SQL, and dbt.
Software Developer Intern
- Designed Snowflake data models and incremental ingestion with Apache Airflow and Snowflake Tasks for near-real-time reporting.
Software Engineering Intern
- Developed ETL workflows for financial transaction data, including profiling, validation, and anomaly detection to support SOX-compliant analytics.
Technical scope
Lakehouse & analytics
Databricks · Apache Spark · Delta Lake · Unity Catalog · Snowflake · dbt Cloud
Languages & integration
Python · SQL · PySpark · Structured Streaming · REST APIs · AWS Kinesis · SFTP
Production engineering
Apache Airflow · Databricks Jobs · Git · CI/CD · Data contracts · Lineage · RBAC · Data quality
Applied AI
LLMs · RAG · Document processing · Structured extraction · Embeddings · Vector search · AI agents
Education
M.S. Software Engineering
Texas Tech University · December 2021
B.S. Computer Science
Texas Tech University · December 2020
CONTACT
Let’s talk data platforms.
Connect with me about enterprise data platforms, technical leadership, or applied AI.