Education & experience.

SEP 2022 — JUN 2026  ·  SEATTLE, WA

B.S. Informatics (Data Science)

University of Washington · Information School ›

Minor in Economics  ·  GPA 3.73  ·  Dean's Annual List

A data-science-focused informatics degree weighted toward databases, data modeling, and applied methods. The economics minor keeps the technical work tied to how systems and incentives actually behave.

Relevant coursework

JUN 2025 — MAR 2026  ·  SEATTLE, WA

Data Engineering Intern

UW Information Technology ›

I worked across analytics and data quality for UW's central IT. I built and automated 15+ Power BI reports and KPIs across four departments, giving business stakeholders self-serve analytics and taking 5+ hours of manual reporting off their week. Underneath the dashboards, I built automated validation and reconciliation checks across source systems (completeness, consistency, anomaly detection) that caught 90%+ of issues and kept records aligned across data platforms. I also analyzed stakeholder requirements against conformed data models and documented data specifications across the data life cycle, so the solutions stayed maintainable after handoff.

Tools used

Lessons learned

• A dashboard that takes five hours of manual reporting off someone's week earns trust faster than one that just looks impressive.
• Catching 90%+ of issues before they reach a stakeholder is what builds real confidence in the numbers.
• Documenting specs against a conformed model is what keeps a solution maintainable once you've moved on.

SEP 2024 — PRESENT  ·  SEATTLE, WA

STEM Remediation Program Manager

UW Accessible Text and Technology ›

I run reporting and tracking across 100+ workflows each quarter, juggling standing deliverables and ad-hoc requests under shifting priorities. A lot of the job is accuracy: digging through large, varied datasets with Python and SQL to find where errors actually come from, which brought downstream data errors down by 25%+. I also documented 10+ data components and specifications that nobody had written down, and automated quality checks that took roughly 30% off manual review time.

Tools used

Lessons learned

• Most data errors trace back to a single source. Finding it beats patching the symptoms downstream, which is where the 25%+ reduction came from.
• Writing down 10+ undocumented components cut ramp time for new staff and killed a recurring class of errors.
• Automating the routine quality checks is where the ~30% of review time came back.

SEP 2023 — AUG 2024  ·  SEATTLE, WA

Employment Data Analyst

King County Labor Council ›

I analyzed employment, wage, and membership data with SQL, Python, and R to give organizers accurate, timely insight that fed directly into strategy. A big part of the role was master data management: consolidating, standardizing, and reconciling data from several source systems into one trusted dataset, resolving misaligned records and documenting the specifications along the way. On top of that I built Tableau and Power BI dashboards and automated the recurring reports, cutting report turnaround by about 40% for organizers and leadership.

Tools used

Lessons learned

• Analysis only matters if it lands in time to inform a decision. Timeliness was the real deliverable.
• One reconciled, trusted dataset removed a whole category of arguments about whose numbers were right.
• Automating the recurring reports cut turnaround ~40% and freed the team for the questions that actually needed a person.