★★★★★ — Former tarot reader, current data reader
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.
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.
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.
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.
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.
Open to freelance projects, full-time roles, and interesting data problems. The cards have spoken: Available for Hire.