I’m a Lead Data Architect in life sciences. My work is the data layer of regulated scientific software: how data is modeled, governed, and trusted, and what it takes to put AI on top of it safely.
I started in a QC lab pipetting samples, which is still the best training I’ve had for thinking about where data comes from. Before my current role, I spent five years embedded inside four of the largest pharmaceutical companies in the world. That’s where I learned that large-scale software succeeds or fails as much through alignment, incentives, trust, and governance as through technology.
Today I help unify more than 100 software products across a $100M+ business unit. Much of that work is getting systems to agree on what their data means: shared vocabularies and definitions, clear systems of record, retention rules that hold up in an inspection, and stewardship with a named person behind it. In a GxP environment, two systems that disagree about a count can become an inspection finding.
The same foundation decides whether AI works. A model reading ambiguous data gives confident, ambiguous answers, and an agent that writes to a regulated system needs the controls a regulator would expect of a person: an audit trail, an accountable owner, and a way to stop it. Much of my time goes into the gap between “the demo worked” and “this is safe to put in front of a regulated customer.”
What I write about here:
- Data modeling and governance in scientific platforms
- Regulated data: GxP, 21 CFR Part 11, audit trails, and retention
- Platform engineering inside enterprise software
- Guardrails for AI and autonomous coding agents
Outside the day job I run a 24-container homelab monitored with Prometheus and Grafana. I trust architectural opinions more when they come from people who have operated their own systems.
If you work on data in a regulated industry and are figuring out where AI fits, I’d like to compare notes. You can find me on LinkedIn, and the now page has what I’m working on at the moment.