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.
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.
LTV:CAC, CAC payback, gross margin, and ARPA by segment. Feature adoption serves as the leading indicator behind the expansion and retention that drive lifetime value.
Bookings by quarter, win-rate trend, an open-pipeline funnel, quota-tier distribution, and a rep leaderboard. These are the surfaces a CRO and FP&A partner share in the forecast call.
Green/Yellow/Red health distribution by segment, support volume against SLA attainment, ticket severity mix, and resolution-time trend. This acts as the early-warning system for net revenue retention.
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 is actually driving growth.
Magic number, CAC payback, burn multiple, and pipeline coverage. These metrics highlight how efficiently go-to-market spend converts into new ARR.
A headcount walk from beginning to ending balance, monthly hiring vs. attrition, annualized attrition by division, and open requisitions. This represents the people side of the operating plan.
Actual vs. budget variance by cost center. This view enables finance partners to analyze OPEX and track performance against the operating plan.
A cash flow statement, runway, and burn multiple built on a fabricated balance sheet and P&L. These synthetic datasets tie together by construction to answer whether growth is self-funding.
I spend my time on the boundary where FP&A and BI engineering meet. The data model, the pipeline, the dashboard, and lately the LLM-augmented workflow that makes the end product usable.
Recently I have 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.
