Two decades between the finance and data engineering teams — modeling, BI, EPM, and the operational systems that turn transactional data into the reports and dashboards that run the business. Now: applied LLMs in the FP&A stack.
Real product surfaces — not slide deck mockups. Each case study runs against a synthetic SaaS company's centralized database to show what FP&A workflows look like when finance, data, and AI sit on the same stack.
ARR waterfall, NRR / GRR retention, MRR movement bridge, and 5-year cohort retention. Designed for the FP&A & CS leaders who run the QBR.
Decomposes the revenue YoY delta into volume, price, and mix effects across products, segments, and pricing models. Built for the CFO who has to answer "what actually drove growth?"
Actual vs. budget variance by cost center, with a writable planning surface — finance partners enter Q3 targets in the UI, the changes save straight to fact_budget.
I spend my time on the boundary where FP&A and BI engineering meet. The deliverable is rarely a spreadsheet — it's the system behind it: the data model, the pipeline, the dashboard, and lately the LLM-augmented workflow that makes the end product usable.
Most recently at Envoy, I designed AI-driven finance workflows — taking GL-level data into a centralized database, and out to the dashboards, spreadsheets, and slide presentations that support monthly close, forecasting, and board prep.
Before that, FP&A and BI data orchestration at larger corporate entities. As a former accountant, I care about reconciliation.
Now: open to finance, analytics, and data roles at companies treating finance and intelligence systems as a product, not an end-of-month obligation.
