Who I Am
A data practitioner who's worked at every layer of the stack — from raw petabyte pipelines to boardroom dashboards.
Data Analyst · Open to Work
I'm a data analyst and engineer with over six years of experience spanning enterprise software engineering at Microsoft and academic analytics at the University of Virginia. I hold a B.S. in Statistics and Data Science from Virginia Tech, which gave me a rigorous foundation in both the math and the code.
At Microsoft, I worked on financial data infrastructure that touched hundreds of petabytes of raw data — building ETL pipelines, financial dashboards, and leading a high-impact migration of SSAS cube infrastructure that delivered a 200x performance improvement. That experience taught me how to operate with precision at enterprise scale.
Most recently at UVA, I focused on the human side of data — working as part of an analytics team where I led individual stakeholder projects end to end: meeting with university partners, understanding their real analytical needs, and building Tableau dashboards that actually got used. It was a great chance to sharpen my communication and collaboration skills alongside the technical work, and to help less technical stakeholders onboard to the dashboards I delivered.
I believe the best data work is invisible — it just helps people make better decisions faster. I'm currently looking for my next role where I can bring this blend of engineering rigor and analytical clarity to a new team.
Experience
Led individual stakeholder analytics projects end to end. Contributed to Tableau site optimization (20% load time improvement) and helped less technical stakeholders onboard to delivered dashboards.
ETL pipeline development at petabyte scale, financial dashboards, SSAS cube migration (200x faster filtering, 300% processing improvement), and desktop-to-web app migration (15% productivity gain).
Ported the Most Recently Used (MRU) feature to a new architecture, enhancing system reliability and performance.
Education
Dual focus on statistical theory and applied data science, with hands-on project work in machine learning, NLP, and data visualization.