Claude Lorrain · Seaport with the Embarkation of the Queen of Sheba · 1648
About
What I believe, how I came to this work, and how I run an engagement.
00Belief
Finance and operations can run meaningfully better with a small, well-placed lift from data strategy and engineering.
At their core, accounting and data are the same discipline; both are about turning raw information into decisions.
01What I Do
I help SMBs and PE-backed companies get the financial and operational clarity that was previously only available to large enterprises.
Most companies already have the raw material: the transactions, the systems, and the people who know how the work actually gets done. What they lack is a clear map of how it all flows together, and the infrastructure to surface it.
So I start not with code, but with people: sitting with the team that runs the close, watching how reporting actually happens, and finding where the hours go and where the knowledge lives.
From that map, I build on what you already own. Rather than replacing QuickBooks, NetSuite, Excel, or your CRM, I lay a shared data model beneath them, one spine every report and dashboard can draw from. The judgment stays with people; the engineering makes the picture clear.
The work starts in the office of the CFO, and the results flow outward: a business that makes faster, smoother decisions, built on connected systems, a shorter close, a clear view of what's driving it, and the busywork automated.
I work as an operator embedded in the business, not a vendor billing hours. When a job calls for a specific data engineering capability or a domain expert I don't have, I bring in the right person for that outcome. Every engagement stays personal, accountable, and direct.
I came to this work through accounting. I studied it because it was the language of business, the clearest way to understand how a company actually works, and always intended to use it operationally, not as a reporting function.
I started in public accounting on the audit side, first at a midsize firm, then at Big 4, with time in PE finance along the way. The credential was valuable, but the pattern underneath it mattered more: the numbers existed and the people understood the business, yet almost none of it reached the decisions that depended on it.
When I left New York and stepped back, I took on work for friends and family: Excel cleanup, financial modeling, basic automation, and a lot of time spent understanding how each business actually ran.
Better analytics led to better questions, and better questions pushed past what spreadsheets could do. To answer them, I began teaching myself the methods and strategies that became data engineering, and how it interacted with finance. That intersection is the work I do now.
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