I clean and connect messy data, test what the numbers mean, and build reporting that helps people make decisions.

SQL · Python · Excel · Power BI · PostgreSQL · Streamlit

Data Analyst resume  ·  Business Intelligence resume  ·  LinkedIn  ·  Email

• Analysis: SQL queries, Python and Pandas, data cleaning, validation, statistical analysis, and investigation of trends and outliers.
• Reporting: Excel KPI reports, executive workbooks, dashboards, metric definitions, and clear explanations of findings.
• Data foundations: ETL, reconciliation, dimensional modeling, and reproducible analytical datasets.

My experience spans operational reporting at R&P Consultants, WHO data analysis at Global Health Impact, and research data workflows at Binghamton University. The projects below show the methods and deliverables in more detail.

Question: Where are the strongest opportunities to reduce 30-day hospital readmissions?

Analyzed 11,720 hospital-condition records from CMS data. Built a cleaned dataset, reusable SQL views, statistical comparisons, and an opportunity score that combines excess readmission performance with patient volume. Deliverables include a Streamlit dashboard and an Excel executive workbook.

Methods: SQL · Python · Pandas · SciPy · Excel · Streamlit

Explore the analysis and dashboard code

Question: How do credit, funding, capital, profitability, and interest-rate trends shape bank risk?

Built a reproducible FDIC and FRED analysis pipeline and a peer-relative risk framework. Used SQL and Python to compare institutions, identify watchlist candidates, and explain risk and performance trends. Deliverables include a Streamlit dashboard and an Excel analysis workbook. The score is a research triage measure, not a regulatory rating.

Methods: SQL · Python · DuckDB · FDIC · FRED · Excel · Streamlit

Explore the risk framework and reports

Question: How can race, lap, stint, and telemetry data become useful race-week analysis?

An interactive Formula 1 product with race reports, driver comparisons, strategy views, and championship analysis. Its data pipeline validates source inputs and builds product views from race results, session context, timing, and telemetry.

Methods: Python data pipeline · TypeScript · React · Supabase · Data visualization

Open the live app · Explore the source

• Municipal payroll comparison — compares 2024–2025 overtime and reported pay in NYC, Chicago, and San Francisco using official city data, with source-specific comparability notes.
• Measles immunization atlas — explores WHO immunization and measles data through interactive maps, country screening, and data-quality checks.
• Historical trading strategy research — tests stock and ETF rules on a shared holdout window and reports model calibration, costs, and research limits.

• Tabularis — SQL workspace and database interface contributions.
• DuckDB Web — documentation fixes for SQL behavior and configuration.
• BeliefState — a DuckDB store backend for durable local state.

I am open to Data Analyst, Reporting Analyst, and Business Intelligence Analyst roles. I am based in Binghamton, NY and open to relocating.

Download my Data Analyst resume · BI resume · LinkedIn · Email