Business Intelligence Engineer · Seattle
I build analytics products that turn messy, real-world data into clear decisions—dashboards people trust, and pipelines that don’t break when things change.
A quick snapshot of how I think, what I enjoy, and why I’m in this work.
I started in operations and bookkeeping, where I learned that clarity matters—when numbers are messy, everything slows down. That pushed me toward analytics: building systems that keep teams aligned and decisions grounded.
Today, I’m focused on analytics engineering and data engineering—strong definitions, dependable pipelines, and stakeholder-ready storytelling.
When I’m not building dashboards, I’m usually learning guitar, traveling, or chasing a new skill that makes me better at building products.
Rocky Mountains
Guitar
Snek study
Building analytics and reporting experiences for sustainability initiatives—bringing together data pipelines, clear metric definitions, and dashboards built for leadership decisions.
Building deeper software fundamentals (data structures, systems design) to complement my analytics and data engineering work.
Worked across product and analytics to make customer sentiment and engagement easier to measure—modernizing dashboards and strengthening the data behind them.
Intensive training across software development, testing, and professional skills—helped me bridge into analytics roles.
Learned the operational side of data: reconciling numbers, tightening processes, and communicating clearly under pressure.
Core strengths and what I'm sharpening now: clear definitions, strong models, honest automation, and collaborative communication.
Real builds that show how I think. Each one includes what I build, what I learned, and what I’d do next.
Small, consistent weekly builds to keep my data engineering skills sharp—clean → model → validate → ship.
A reproducible pipeline that turns raw geopolitical indicators into curated tables and narrative insights for trend and driver analysis.
An end-to-end exploration of Seattle’s clean energy transition using public datasets—cleaning, modeling, and visual storytelling to surface trends and drivers.
A small-but-real pipeline that ingests JSON, cleans it, standardizes schema, and publishes curated tables for analysis.
A text-to-insights workflow that extracts sentiment and action items from feedback and returns structured JSON for downstream reporting.
Let’s connect — I respond fastest by email.
Open to thoughtful collaboration, feedback, and knowledge sharing across analytics, BI, and data engineering.
