What I learned from analyzing live-commerce growth.
Category mix, show timing, and sports momentum — what supported a 4-month follower scale-up.
read · coming soonData engineer, 2+ years on Snowflake pipelines, SQL validation, and production data workflows. Starting Northeastern's MS in Business Analytics in Fall 2026 — building toward analytics roles in commerce, sports, marketplaces, and consumer products.
I grew up between two obsessions — cricket and computers. The first paid off early: USA U19 prospect pool, four seasons in Minor League Cricket. The second is the longer arc, and the one I'm doubling down on now.
At Rutgers I studied Computer Science with a minor in Entrepreneurship. The CS gave me the tools; the entrepreneurship minor gave me a reason to use them on real problems. The first one was a friend's sneaker drop community — about 350 members — which became my first analytics case study: a Google Sheets tracker for engagement, retention, and inventory patterns.
After graduation I joined Bahwan Cybertek, a Snowflake premier services partner. Two and a half years on production migrations, validation, and KPI dashboards. Solid engineering. But I kept circling back to the questions the data could answer, not just the pipes that moved it.
That's the pivot. Northeastern's MS in Business Analytics starts this fall. Until then I'm in self-directed prep — SQL practice, Python and statistics, and a friend-operated live-commerce analytics case study where I'm studying how category mix, show timing, and sports momentum supported audience growth from 0 to 7K followers in 4 months.
The plan: a 2027 co-op at a sports, betting, marketplace, or consumer-product company that takes data seriously and ships fast. Long-term: be the person on a product team who can both read the data and understand the customer.
Capability framing rather than role framing — these are the things I can already do well, and the lens I bring to a team.
Production ELT into Snowflake with reconciliation and lineage built in from day one. 10+ flows live at Bahwan, validation cuts post-load issues by ~20%.
Looking at messy data and finding the question worth answering — category mix, retention cohorts, profit-per-hour, growth driver patterns. Python + pandas in notebooks.
KPI views leadership actually checks. Built weekly performance dashboards from platform logs at Bahwan that cut data issue detection cycles by ~35%.
Reading data and customer behavior at the same time. Drawn to consumer, sports, live commerce, and marketplace problems where engagement signals are as interesting as financials.
Three project contexts where I apply data, product, and analytical thinking. A friend-operated live-commerce business growing its audience from zero. A friend-run sneaker community studied through a lightweight tracker. And Hothand — a synthetic-data analytics engine I built to explore how sports performance signals translate into seller-facing decisions on a sports card marketplace.
An anonymized analytics case study using data from a friend-operated sports card live-commerce business. Studied how category mix, show timing, sports momentum, and inventory decisions supported audience growth from 0 to 7K followers in 4 months.
A product and analytics case study focused on sneaker release behavior, community engagement, inventory tracking, and retention signals using a lightweight tracker and analysis framework.
A sports card marketplace analytics project that turns player momentum, card scarcity, listing saturation, sales velocity, and engagement signals into seller-facing decisions: List Now, Watchlist, Hold, or Avoid. Includes an Opportunity Score, price band recommendations, and a live show planner. Synthetic data only.
Hover any tool to see which methods it powers. The connections matter more than the inventory.
Pre-MSBA self-directed prep. Roughly fifteen hours a week of structured learning on top of the existing schedule.
Short writeups I'm drafting on the way to publish. Three at a time, no more — the queue stays honest.
Category mix, show timing, and sports momentum — what supported a 4-month follower scale-up.
read · coming soonMost "the dashboard is wrong" tickets are actually upstream data tickets.
read · coming soonWhen the more interesting work is downstream of where you're sitting.
read · coming soonCricket isn't a hobby footnote — it's how I've spent half my life. The discipline transfers more than I thought it would: preparation, calm under pressure, reading patterns in real time. All things product analytics rewards.