About

I started in mechanical engineering. An interview at Siemens turned me toward data. That detour became business school, then a few startups, then a decade building the data infrastructure behind business decisions.

Since October 2025 I have been the client-facing technical lead on four engagements across Entertainment, Finance, Healthcare, and Real Estate. Two or three run at once, because the next engagement starts before the last one closes. I wrote the technical case behind a ~$1.5MM Microsoft and Databricks ECIF proposal. Before consulting: at NC DIT I architected Delta Live Table pipelines that delivered statewide transportation data 30% faster than the legacy ETL; at Snap One I helped drive a 50% EBITA increase (~$50M) by building the pipelines and the governance framework behind them, and certifying the metrics leadership acted on; at Celonis I turned enterprise process data into ~$10M business cases executives would actually fund.

My focus now is AI-native data engineering: lakehouse platforms designed for the agentic era, and using AI to accelerate the engineering work itself. In practice that means a Databricks engineering catalog other engineers build inside, and agentic systems on Model Context Protocol and the Claude Agent SDK that automate the translation and validation a migration otherwise repeats by hand, and the deployment that follows. I am looking for Principal work where the mandate is the platform and the practice, not the next pipeline.

Outside the job I box, dance salsa, bachata and merengue, and build furniture in a shop I document the same way I document a lakehouse. 15 countries so far, most of them in Latin America and the Caribbean. The through-line, if there is one, is a preference for practices with an honest feedback loop: the round, the dance floor, and a joint that either closes or does not.