I build data pipelines and statistical analysis systems for public health and international development: reproducible, configuration-driven workflows that ingest, harmonise, and analyse survey, administrative, and clinical research data in Python, R, and Stata.

By day I build and maintain the data infrastructure behind longitudinal clinical research at the University of Florida: automated REDCap extraction tooling, federal data-submission pipelines, and standing data-quality audit layers. I also consult through A3DI, helping UN agencies and NGOs turn messy programme data into scalable, maintainable, production-ready systems, with work spanning food security, nutrition, and social protection across more than a dozen countries in Asia and Africa.

🔧 Data Pipelines — Config-driven, reproducible pipelines that ingest, transform, and harmonise survey, administrative, and clinical data across sources. Built to extend, not to rebuild from scratch when the next round arrives.

🧪 Research Data Systems — REDCap-centred data engineering for clinical research: reusable API extraction packages, automated validation and QA sweeps, and pipelines feeding federal repositories (NIMH Data Archive).

🔁 Code Migration — Move legacy analysis from spreadsheets, SPSS, or ad-hoc scripts to production-quality Python or R. Preserve your statistical logic, improve everything around it.

📊 Applied Statistics — Methods your reviewers will trust, code your team can maintain. Complex survey designs and disaggregated analysis across demographic and geographic stratifiers.

Uncovering Who Is Furthest Behind →
Multi-country MCHN equity analysis for Alive & Thrive — surfaced a 35pp equity gap across 15+ survey rounds in 3 countries.
code · Python · Survey Analysis · Pipeline

Data Quality Screening at Scale →
Automated quality checks for UNICEF JME — 70% of surveys auto-cleared with zero manual review across 1,091 surveys in 167 countries.
Stata → R · Pipeline · Data Quality

Informing National Fortification Policy →
Multi-source food consumption analysis (Namibia NHIES) — revealed a 10× regional spread in nutrient intake to inform national fortification policy.
code · Python · Survey Analysis · Policy

My clinical research data work at UF lives in institutional and private repositories; happy to talk through the architecture.

Core   Python · R · Stata · SQL
Data & pipelines   pandas · NumPy · PostgreSQL · SQLite · REDCap · ODK/KoboToolbox · REST APIs · GitHub Actions
Analysis & viz   Tableau · Power BI · matplotlib · seaborn · Jupyter
Engineering   Git · Flask · Bash · Linux · HTML/CSS

Sharpened my software engineering fundamentals with computer science coursework (data structures, computer architecture, discrete math) at Oregon State University.