01 — Descriptive
Make patterns clear
Compelling visualizations, interaction matrices, and storytelling frameworks that make patterns instantly understandable.
BI Engineer · Causal & Decision Intelligence
Turning complex data into confident decisions
I design and build robust data systems that turn raw data into reliable foundations for advanced causal analysis — and transform complex datasets into clear, actionable strategic insights.
About
I'm a BI Engineer with deep expertise in Causal & Decision Intelligence. My work spans three interconnected layers of analytics — from making patterns understandable, to explaining why outcomes occur, to recommending concrete actions.
02 — Diagnostic
Explain why
Rigorous causal inference — DAG construction, Bayesian statistics, and counterfactual analysis — to uncover true root causes.
03 — Prescriptive
Recommend action
Game theory, optimization, and strategic modeling to recommend concrete, measurable actions.
Currently
BI Engineer at Meet5
I build end-to-end analytical infrastructure — BigQuery pipelines, retention & revenue tracking systems, and interactive Dash applications — that powers both operational reporting and high-impact causal research. I also work in geospatial analysis (H3, Kepler.gl) and complex network analysis (NetworkX). Beyond technical execution, I'm committed to mentoring and elevating analytical thinking, combining scientific rigor — inspired by Popper's falsifiability and Pearl's causal framework — with practical business acumen to move teams from data confusion to confident, evidence-based decisions.
Featured Projects
Synthetic dataset · Simulation · PyPI package
grocery-sim
A structural microsimulation of a small grocery store — a business dataset with a fully known causal ground truth. Instead of drawing columns from convenient distributions, it builds a whole neighborhood economy first — customers with budgets and price sensitivity, an owner solving a real assortment-and-location problem, a daily market where prices and promotions adapt to what happened the day before — and lets receipts, invoices, and the monthly ledger fall out of what that world did.
A real-decision micro layer (purchases, pricing, hiring) is kept strictly separate from a pre-scripted macro layer (weather, holidays, shocks, taxation), which makes counterfactuals exact: drop a scripted event, replay with the same random draws, and the difference is the causal effect — not an estimate. On top sits an 8-layer analysis catalog (cleaning, forecasting, optimization, causal/structural modeling) gradeable against a hidden answer key, plus a full paper and worked client case studies.
Excel
Python
PostgreSQL
