01 — PROFILE
Seventeen-plus years of data engineering for large-scale, high-stakes operations: ETL architecture, geospatial solutions, RPA, and BI across ERP, master-data and GIS systems of record. Python, SQL and R on one side; regulators, auditors and field crews on the other — I build the data layer both sides can rely on. B.S. in Computer Science & Computer Engineering.
02 — ACCOMPLISHMENTS
Engineered Python ETL with parameterized, reusable components that automate correlation and discrepancy detection across enterprise ERP and master-data systems — cutting data remediation cycle time 30% while processing thousands of records per cycle.
Kept GIS and ERP systems of record in lockstep for large-scale field operations — validating, correlating and correcting asset data to sustain 99%+ accuracy across 160+ monthly work notifications, enabling faster, data-driven field decisions.
Took an automation pipeline from business case to enterprise adoption — a UiPath RPA solution processing 5,500+ bills of materials annually with an estimated $400K+ in avoided costs, built on parameterized, reusable components.
Applied ontology-based data models in Palantir Foundry to analyze field equipment hierarchies and characteristics — surfacing insights that feed reliability and asset-risk decisions across large equipment fleets.
Designed Data-as-a-Service dashboards and performance reporting that gave senior leadership real-time operational visibility — 21 dashboard enhancements delivering a 40% faster reporting cycle.
Developed repeatable ingestion, classification and retrieval workflows for a 125,000+ record enterprise database — 99.98% on-time processing against a 99% target, with zero compliance findings across eight years of audits.
End-to-end analytics capstone on 6.2M bike-share rides — a Python (pandas) cleaning pipeline with logged, assertion-checked transformations (0.10% of records removed, every decision documented), revealing that casual riders ride 58% longer and nearly double their volume on weekends. Delivered as a six-view Tableau dashboard and three data-backed conversion recommendations.
EXPLORE THE INTERACTIVE REPORT ↗A zero-cost production pipeline running right now: every 30 minutes it ingests Divvy's live 2,000-station feed, enforces five blocking quality gates, transforms with DuckDB SQL, and publishes to a live dashboard — fail-loud validation, bounded storage, public run log. The real-time sequel to the Cyclistic analysis.
OPEN THE LIVE DASHBOARD ↗03 — SKILLS & STACK
04 — EDUCATION & CERTIFICATIONS
05 — NEXT ROLE