Over 9 years turning ambiguous business problems into working deliverables at global scale. Now applying that same discipline to building enterprise quality tools with AI. Folsom, CA.
Client facing business operations and technical delivery leader with over 9 years turning complex business needs into working solutions, systems, reporting, and dashboards at global scale. I lead from
discovery to delivery. I use traditional and Agile project management methods to uncover the root of a stakeholder request, structure the solution, and drive the deliverable across
global teams. My processes are backed by PMP and Lean Six Sigma program discipline with MBA level financial acumen.
More recently, I've brought that same approach into building tools
with AI. I designed and shipped an enterprise level revenue calculation dashboard at Oracle using Codex. I independently built an AI job matching platform, picking up Claude Code, Vercel, GitHub, Next.js, and Supabase along the way.
I'm comfortable presenting and influencing from analyst to executive. In practice, that means
translating stakeholder requirements into something development teams can actually build, testing deliverables before they ship, and managing both analysts and developers day to day.
When Oracle acquired Cerner, transitioning sales reps had no automated compensation solution. Reps were being paid manually based on legacy system data while the business waited on a fix. I led the effort to migrate legacy customer contract data into Oracle's systems, set up the systems to allow for new bookings and build the automation and calculation logic to pay them correctly. I worked across development, reporting, sales, incentive compensation, and administration to get it done.
At a PI planning session, the team had two options that needed the same development effort. One was automating a GTM change. The other was fixing a long standing gap that looked less urgent, but was quietly costing the team several hours of manual review each month. Skipping the automation of the new policy change would only touch a few orders a year, so I pushed to prioritize the older gap in the backlog instead.
For years, the regional comp admin teams had been logging several hundred compensation issues for review, each one taking hours to manually calculate and review. While we had templates and calculation guides available for regional comp admin teams, this was still a slow, highly manual ongoing effort. With no engineering background, using Codex as my coding assistant, I built a compensation calculator and revenue classification dashboard using all of Oracle's policy and calculation methods to ingest contract data and calculate results.
The calculator is a deterministic model, built on two parallel calculation engines using a stateful sequential walk. Prior to publishing for enterprise use, I validated the logic against hundreds of contracts with known, precalculated results, checking every output line by line, before signing off with compensation, development, and senior leadership.
A full stack AI job application platform for tech workers, built solo around a human in the loop model: "apply smarter, not more." Every morning, AI scans postings and scores them against your background; you review a daily digest and approve before anything is submitted.
An enterprise tool built with Codex that ingests contract data and classifies ATR, ARR, renewal, and expansion revenue in seconds. Replaced a manual review process handling hundreds of issue trackers a year, each taking up to an hour to calculate and review manually. Validated against hundreds of precalculated test cases before rollout.
→ Try an interactive recreationThis demo is a recreation built from scratch for this site, using synthetic sample data and simplified logic I wrote for demonstration. This demo contains no code from the tool built for Oracle and does not run on Oracle's real policies or classification rules. Run the walk below to see how each engine classifies a line as new, renewal, or expansion, and splits its value accordingly.