I turn messy operational data into systems that report themselves.
1.5+ years automating reporting and building BI systems across engineering operations and gaming analytics, using SQL, Power BI, BigQuery, Python, and Google Apps Script.
Analytics, with automation built in
I'm a Data Analyst who got here through operations, not a classroom. I spent my first year in Live Ops for a fantasy sports product, then moved into engineering program analytics for a global SaaS company. Both jobs taught me the same lesson: reporting only stays useful if someone doesn't have to rebuild it by hand every month.
My work sits at the intersection of SQL, Power BI, BigQuery, and Python. I build the dashboards, and I also build the pipelines that keep them accurate without a human checking every row. That combination is what let me support reporting across 72 engineering squads and 200+ Jira projects without a dedicated reporting team behind me.
I think of myself less as a person who makes charts and more as someone who designs the plumbing behind them, which is why I'm equally comfortable being briefed as a Data Analyst, a Data Engineer, or a Business Analyst, depending on what a team needs most.
Data Analysis
SQL queries, cohort and funnel analysis, A/B testing, and Power BI or Tableau dashboards that turn raw tables into decisions.
Data Engineering
ETL pipelines in Google Apps Script and Python, BigQuery data modeling, and validation logic that keeps messy sources reliable.
Business Intelligence & Ops
Stakeholder reporting, KPI design, and SOPs that keep 27+ cross-functional partners aligned on the same numbers.
Tools I reach for
Data Analysis & BI
Programming & Automation
Data Engineering
Analytics Techniques
Where I've worked
Data Analyst, TPM Operations Consultant
- Built an Apps Script ETL platform automating Monetization and Feature Velocity reporting, consolidating OKR and Jira-sourced data into leadership-ready datasets each month.
- Built Power BI, SQL, and BigQuery dashboards for 4+ teams, cutting reporting prep time by 80%.
- Partnered with 27+ stakeholders to translate reporting requirements into standardized dashboards.
LiveOps Data Analyst
- Ran cohort and funnel analysis in SQL to identify drop-off points in player engagement.
- Built a role-aware Player Card Validation system, cutting QA review time by 70%.
- Built a Python/Selenium scraping platform for tournament data, cutting manual entry by 90%.
Selected work
Enterprise Monetization & Feature Velocity Automation
Every month, someone had to manually pull data from the OKR tracking sheet into a separate Monetization sheet, two spreadsheets with no consistent structure between them, plus a separate manual pass to categorize Feature Velocity initiatives across squads.
Built a Google Apps Script pipeline that consolidates OKR sheet data into the Monetization sheet automatically, using dynamic header alias mapping and validation logic that preserves formulas and dropdowns, alongside automated monthly Feature Velocity categorization.
Cut manual reporting effort by over 90% and gave TPM and Product teams a consistent, leadership-ready dataset every month.
Engineering Delivery Analytics Platform
TPMs had no single place to see how their squads' Jira projects were progressing, and pulling together a status view for leadership meant checking Jira manually, squad by squad.
Built a pipeline that pulls Jira project data for 72 squads through BigQuery and MySQL into a scheduled Google Sheet, auto-updating whenever a project changes or a new one is added, then layered a Power BI dashboard on top mapping each TPM to their squads with per-squad progress, blockers, and delays.
Gave TPMs a live, self-updating view of their squads' Jira work and a ready-made dashboard for reporting progress to leadership.
Tournament & Player Data Automation Platform
Squad and tournament data had to be re-entered by hand ahead of every match, which was slow and error-prone during live tournament windows.
Built a three-module Selenium and BeautifulSoup scraping platform with dynamic scrolling for JS-rendered pages, normalizing data from multiple sources.
Cut manual data entry by 90% and fed clean, structured datasets directly into downstream player-card validation.