** Financial Data Analyst | Product · Operations · Business Analytics | SQL · Python · Snowflake · Power BI · Databricks
I turn messy data into decisions that stick.
Two years ago I built the CFO forecast cycle for a $1.8B public utility — tracking budget vs. actuals across 7 business units for 6 department heads. That's when I understood what financial data actually means: real money, real decisions, real consequences when the numbers are wrong.
More recently I identified $111K in revenue leakage across a food manufacturer's multi-market supply chain and built an automated ETL pipeline that eliminated 15+ hours/week of manual reporting. And I saved a payments company $513K by proving their $537K fraud review expansion would prevent only $24K in fraud.
I build pipelines, dashboards, and analytics systems that drive decisions — not just reports.
| What I Built | Result |
|---|---|
| CFO forecast cycle — $1.8B annual operating plan | Accuracy improved from -17% to -7% |
| Prophix rebuild with star schema — $600M infrastructure budget | Monthly close 3 days → same day; accuracy +30% |
| Supply chain ETL pipeline — multi-market operations | $111K leakage identified; 15+ hrs/week reporting eliminated |
| Fraud risk platform on Snowflake — 285K daily transactions | Prevented $513K net operational loss |
| Power BI dashboards replacing 5 manual Excel reports | 6 hrs/week saved; lag cut to real-time |
| DAU/WAU/MAU retention analytics — 1,000+ users | 30% adoption growth in 3 months |
| Informatica ETL migration — 40+ SQL validation queries | Zero defects at go-live; defect rate cut 85% |
Languages
Cloud & Data Platforms
ETL & Pipelines
Visualization
Tools
Methods
Automated analytics platform for a food manufacturer expanding from Dallas to New Jersey — built to identify revenue leakage and assess expansion readiness.
- Identified $111K in revenue leakage (3.7%) driven by fulfillment gaps and inventory allocation inefficiencies
- Eliminated 15+ hrs/week of manual reporting via automated ETL pipeline (n8n + PostgreSQL + Python)
- Flagged Dairy category (79.5% of revenue, 47.7% OTIF) as critical expansion blocker — shifted leadership focus from growth to operational fix
PostgreSQL Python n8n SQL Star Schema Power BI
Post-acquisition data integration platform unifying $119.93B in combined revenue across Finance, Sales, Marketing, Supply Chain, and Executive teams.
- Designed Databricks Lakehouse with Medallion Architecture (Bronze/Silver/Gold) using PySpark + Delta Lake + AWS S3
- Eliminated 40+ hrs/week of manual Excel reporting with <5 min daily dashboard refresh
- Delivered strategic recommendations: 15–20% delivery cost reduction, 10–15% LTV increase, $2–3M stockout prevention
Databricks PySpark Delta Lake AWS S3 SQL Star Schema
Snowflake-based fraud risk platform analyzing 285K+ daily card transactions with transparent risk tiering and cost-benefit modeling.
- Found 98.97% of transactions carried low fraud risk at a 0.17% loss rate
- Cost-benefit analysis on proposed $537K manual review expansion showed net $513K loss — recommended $50K automated MFA instead
- Built 3 Power BI dashboards presenting findings as a transparent alternative to black-box ML
Snowflake Python SQL Power BI Cost-Benefit Modeling
M.S. Computer Science — Westcliff University, Irvine CA · Summa Cum Laude, 3.97 GPA · June 2025
B.A. Economics — California State University, Sacramento CA · 3.69 GPA · May 2022
🥇 Top 50 Global Finalist — IBM watsonx Pre-conference Hackathon
🎓 Summa Cum Laude · Dean's List (All Semesters 2023–2025)
🏅 Founder's Scholarship for Computer Science (Graduate)
