I've spent my career inside the data environments most people only hear about in postmortems: multi-million row fund and accounting systems at major investment firms, where a wrong assumption doesn't just cause a bug — it shows up on someone's statement.
That work taught me to look past what a system is supposed to do and find out what it's actually doing. Undocumented dependencies. Quiet technical debt. Integrations held together by something nobody can quite explain anymore. I've mapped it, untangled it, and helped firms move it forward — transforming legacy, on-premise environments into cloud-engineered, API-driven, AI-ready ecosystems using medallion architecture.
That's the lens I bring to every engagement: diagnose first, prescribe second.
I don't sell a platform, and I don't staff a build team. That independence is the point — when I tell you what's wrong with your data environment and what it'll take to fix it, there's no hidden incentive shaping the answer.
I tailor every recommendation to the realities most consultants wave away: your actual budget, your existing IT staff, your company's risk tolerance, the internal politics of change, and how ready your organization really is to adopt something new. Often, that means starting small — a single dataset, a contained proof of concept — so the case for change makes itself before anyone has to take it on faith.
Good diagnosis is a discipline, not a guess. The name reflects how I approach every engagement: rigorously, methodically, and without assuming I already know what I'll find before I look.
A fixed-scope assessment that tells you, clearly, what's really going on underneath your data.