I build analytical tools — dashboards, financial models, and data workflows — that help operators and finance teams move faster with sharper numbers. Below are two projects that show how I think about marketplace economics and SaaS modeling.
The dashboard side takes operational data and surfaces what matters. The modeling side takes operator assumptions and projects them forward. Together they're the two halves of finance & analytics work.
Interactive analytics dashboard for a fictional B2B services marketplace. Five analyst views (Overview, Growth, Unit Economics, Cohorts, Categories), period and category filters that slice the data live, and context-aware KPIs that reframe per view. Data is synthetic but the story is deliberate — a Q3 take-rate compression, a post-launch retention step-up, and a cohort-level LTV:CAC trend.
Driver-based three-year model for a fictional B2B SaaS. Customer acquisition, retention, ACV, and opex ratios flow through an ARR waterfall into a quarterly P&L. Scenario toggle (Base / Bull / Bear) updates all downstream metrics. Zero formula errors; industry-standard color conventions throughout.
I'm a finance and analytics professional with experience across fintech and marketplace businesses. My work tends to sit at the seam between the P&L and the operating metrics — building the tools that let teams answer "what should we do?" rather than just "what happened?"
I care about craft: models that are auditable down to the cell, dashboards that load fast and make the question obvious, and analyses that don't hide behind jargon. Day-to-day, I work mostly in SQL and Excel, with dbt and Airflow for data pipelines and Tableau or Looker for dashboards. I also lean on AI tools, Claude and GitHub Copilot, to draft queries faster, stress-test model logic, and catch errors earlier.
I'm always happy to chat about analytics, financial modeling, or a specific problem you're trying to solve.