I read the data.
It tells me things.
A dataset is a story waiting to be read. A chart is just a stitched grid.
Currently in product operations, I sit where business and data meet, turning Jira backlogs, sprint work, and messy ticket data into the insights and process improvements that help teams make better decisions. I work fluently across SQL, Python, and Tableau, partner closely with product managers on prioritization, and use AI-assisted tools to move faster, so more of my time goes to what actually matters: asking the right questions and telling the story the data is trying to tell.
The Stash — My Stack
Every maker keeps a stash. Here's what's in mine.
Finished Objects
Completed makes. Each one started as a tangle and ended as a pattern.
A methodology walkthrough on a synthetic ticket queue I built from scratch — a demonstration of how a product operations analyst turns a messy backlog into insight. Using a Jira-style export, Excel cleanup, and pivot tables, I work through volume trends, category shifts, and a subtle finding buried in the priority field. Built on invented data; the focus is the process, not any real company.
→ Read the Walkthrough ↗Started with a question: which regions and deck archetypes actually dominated 21 years of competition, and how did the meta shift over time? To answer it I designed a normalized PostgreSQL database (18 championships, 36 players, 26 archetypes), wrote the SQL to surface the trends, and visualized them in Tableau — with Docker for a reproducible setup. Swap "decks" for products and "regions" for markets, and it's the same competitive-performance analysis a business runs on its own data.
→ View on GitHub ↗I treated my own reading history (485 books) as a dataset a stakeholder might care about, then asked what's actually worth knowing: what drives a high rating, how habits shift over time, where the patterns break. Built in a Jupyter notebook with Pandas and 9+ visualizations — but the real exercise was turning a vague "what's going on here?" into specific, answerable questions, and presenting the answers so a non-technical reader gets them at a glance.
→ View on GitHub ↗A market-analysis dashboard treating a collectibles market like any other product market: what's appreciating, what's losing value, and where popularity and price diverge. Using Tableau and Excel on 2020–2026 data, I tracked price trends, estimated unit sales, and collector-value gaps — the same demand, pricing, and segment-performance questions an analyst asks of any business. Cleaning messy real-world data into something decision-ready was half the work.
→ Read the Blog ↗The Maker
I'm happiest mid-project: a romance novel open on the arm of the chair, yarn in my lap, and some half-finished pattern I swore would be quick.
It turns out reading and making are the same instinct as analysis. You take something raw — a story, a skein, a spreadsheet — find the structure underneath, and follow it row by row until it becomes something. A dataset is just a story waiting to be read; a chart is just a stitched grid.
By day I work in product operations, building toward business and data analyst roles where asking the right question matters as much as writing the right query. When I'm not doing the data work, you'll find me reviewing books and stitching their best lines into embroidery.
Let's Make Something.
(Or Collaborate.)
Open to freelance projects, full-time roles, and interesting data problems.
> status: available for hire ▍