15+ years at Workday, Lyft, and beyond — now building what fintech lending teams actually need: end-to-end data models, SPV reconciliation, risk analytics, and investor-grade reporting. My flagship project LoanLens models 10,000 loans and 1.35M payment events with <0.1% reconciliation tolerance.
Fintech data teams face a specific set of problems: loan portfolios that must reconcile to the cent, covenant monitoring that cannot miss, and investors who need to trust the numbers before they fund the next facility. I've spent 15 years building data systems that earn that trust.
At Workday I lead BI Analytics for a $8B+ marketing org — the discipline of modeling revenue attribution, attribution windows, and budget pacing maps directly to lending portfolio analytics. At Lyft I built growth data infrastructure at scale. Before that, supply chain analytics at Intuitive Surgical, where every number had regulatory implications.
LoanLens, my most recent build, is a simulated Series C lender data platform: 10,000 loans, 1.35M payment events, 3 SPVs modeled in dbt, reconciled end-to-end, with Claude-narrated investor memos. That's the kind of data infrastructure fintech teams need, and I can build it.
I'm actively talking to fintech teams building the next generation of lending infrastructure, portfolio analytics, and investor-grade data platforms. I can move fast and I know how the numbers need to close.