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.

From October 2025 to September 2026 I was the client-facing technical lead at Lovelytics on four engagements across Entertainment, Finance, Healthcare, and Real Estate. Two or three ran at once, because the next engagement started before the last one closed. I wrote the technical case behind a ~$1.5MM Microsoft and Databricks ECIF proposal. Before that: 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. At Lovelytics that meant a 118-skill Databricks accelerator other engineers built inside, written once and reaching their coding agents over four surfaces. It also meant 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. In September 2026 I joined Climb, a Databricks-native Data and AI consultancy, as a Senior Data Engineer.

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.