Every piece starts with a question
Samples of real work across data engineering, analytics, and marketing intelligence: warehouse architecture, identity resolution, attribution, BQML value modeling, market analysis, and a crowdfunding launch I ran myself.
Raw events to revenue
The whole architecture on one page: raw sources through staging, Kimball marts, identity, BQML, and attribution, ending in ad platforms bidding on modeled value. One pattern, run at two very different scales.
Identity resolution
The same identity-resolution work looked different at two scales. For one business, it unified nearly 50,000 identities from fragmented contact records. For a global enterprise, a probabilistic layer connected 98% of the pipeline value missing from the CRM.
Attribution
For one business, moving attribution into the warehouse lifted the monthly won-deal rate 53.4%. For an enterprise funnel, the same shift moved budget out of reach campaigns and into search, where it actually earns its credit.
Value-based bidding
A BQML model scores each lead by predicted value across roughly 1,100 learned feature categories, so Google's bidder optimizes toward that value instead of raw lead counts. Switching to value-based bidding grew one business's monthly revenue rate by nearly 60%.
The Amazon diagnosis
A brand that owned its Amazon niche kept losing ground off-platform. 28M rows across 16 sources showed why: the market had moved, and competition was the story.
Five forces, measured
Porter's five forces scored from 28M rows of consolidated market data. The structural read explained why a healthy Amazon brand kept losing ground everywhere else.
Decisions from data
45K orders re-sequenced into buyer journeys: premium-entry buyers carry roughly 7x the lifetime value, and 16% of buyers drive 48% of revenue, for a genealogy education company.
The crowdfunding pre-launch
A $13.4K pre-launch engine, 4,277 leads at $3.06 each distilled into a VIP core, that funded a Kickstarter 2,070% over goal.