I've spent my career at the intersection of business and data: scaling services and operations at payments and banking SaaS companies, with analysis at the center of how those teams ran. In 2019 I stepped away to trade full-time, building my own market-data pipelines, research, and dashboards end to end. I'm now looking for senior, hands-on roles where judgment about data drives the work, without the overhead of running a department.
I've spent my career turning data into decisions. From 2005 to 2019 I ran services and operations at four SaaS companies in banking, payments, and insurance — ACI Worldwide, Alkami, Decideware, and One Inc — and analysis was always at the center of the job. At One Inc, I designed the Tableau dashboard suite that put Sales, Delivery, Support, and Success on the same numbers. At Decideware, I helped develop and sell a Tableau advisory service that let our Fortune 500 clients analyze their own advertising spend; it grew revenue 98% in a year. At Alkami, a VC-backed startup, I led capacity planning, building the labor models that kept project delivery and revenue recognition on track with board plans while the company doubled its headcount in six months.
In 2019 I left to trade full-time, where every decision gets tested against data and real money. I built my own market-data infrastructure and research process; my ES futures data pipeline shows the standard I hold analysis to. I'm now looking for senior individual-contributor roles where judgment about data drives the work, not producing reports for someone else to interpret.
The dataset behind all of my market analysis: quarterly E-mini S&P 500 futures contracts stitched into one continuous 1-minute series, with daily value-area reports. Measuring volume across seven contract rolls showed liquidity moves four days later than the textbook roll date, so the pipeline rolls on actual volume instead.
A live web app that turns FINRA's daily off-exchange short-volume data, about 12,000 symbols a day, into an interactive treemap, with a daily panel that flags volume surges and short-% shifts against each stock's own 20-day average. I also ran a five-method statistical study of whether short-volume % predicts returns. It doesn't, and knowing that was worth the work.
Python, SQL, Pandas, Tableau, Jupyter, D3.js
Revenue ops, retention strategy, team scaling, process design
HubSpot, Salesforce, JIRA, Confluence, Atlassian suite