Analytics that keeps working after I walk away.
Production GenAI and the dimensional models underneath it, built at 7,000-location restaurant scale and in regulated consumer finance.
Every number below carries its receipt.
how these numbers are measured
- 1. Franchise + corporate reporting scope of a 12-product analytics portfolio at a national restaurant enterprise, 2025–present.
- 2. Insufficient-funds fees avoided by an at-risk-account ML model in FDCPA/CFPB-regulated consumer finance; dollar impact tracked for 12 months post-launch.
- 3. Time-to-response on data-integrity alerts, before vs. after automated anomaly detection with owner routing; self-measured across the incident log.
- 4. 1,014 public actors across six Apify storefronts at last count (Aug 2026), revenue continuous since early 2026; every storefront is linked below, judge for yourself.
Selected work
Three systems, three kinds of proof: commercial, production, and open.
Paid AI agents on the Apify marketplace
I design, build, and operate 1,000+ published automation agents sold as paid products: input UIs, API endpoints, pricing, and maintenance all owned end to end. Real paying users, real uptime obligations, revenue continuous for 6+ months. Python, built extensively with Claude Code, version-controlled in Git.
A marketplace with paying customers is the receipt. Anyone can claim they build AI agents; customers who pay twice are harder to fake.
Natural-language data agent over a semantic layer
For a 7,000-location restaurant enterprise, I shipped a production GenAI product (LLM sentiment analysis + ML classification over customer feedback) and deployed a self-serve natural-language data agent: operators ask questions in plain English and get answers grounded in a governed semantic model: business concepts translated into structures an LLM can reason over.
LLM-as-judge orchestration pipeline
Personal data infrastructure: a pipeline that scrapes 30 heterogeneous sources (Workday, Phenom, Oracle Cloud, Greenhouse, custom APIs), scores every record with an LLM against a versioned rubric behind a validated, typed verdict contract. Qualified results route straight to alerts: signal separated from noise before a human ever looks. Under the hood: idempotent state, atomic writes, retry caps with loud give-ups, prompt-injection fencing, and an append-only judgment ledger stamped with the rubric version that produced each verdict: the feedback loop that lets the rubric improve against real outcomes.
- Parallel orchestration: 24 independent lanes on a thread pool, rate-limit-aware per host; the same topology also expressed as an Airflow DAG with dynamic task mapping and pooled concurrency
- LLM rigor: fenced untrusted input (injection-tested), envelope validation, verdict normalization, double-sampling on positive verdicts
- Reliability: a record can be delayed, never silently lost
The stack
What the projects run on.
data modeling & sql
Data models with defined grain and data contracts; window-function and CTE-heavy transformations; semantic and metrics layers (Lightdash, Microsoft Fabric); source-of-truth documentation teams actually align on.
applied ai
Production GenAI (LLM + ML), custom agents on the Anthropic API, prompt engineering, natural-language data interfaces, AI-orchestrated automation. Certified: Microsoft Fabric Analytics Engineer, DP-600.
analytics that decides
Operational and workforce KPIs against formal OKRs, demand forecasting on 5M+ row datasets, anomaly detection with root-cause routing, marketplace and e-commerce analytics, ML with tracked dollar impact, executive storytelling in Tableau and Power BI.
About
I'm a Senior Business Intelligence Analyst who ended up somewhere more interesting: the seam between analytics and applied AI. Seven years across restaurant operations at national scale, regulated consumer finance, distribution, and e-commerce taught me the same lesson four ways: an analysis only matters if it changes a decision, and a system only matters if it survives contact with real users.
So I build for survival: metrics with governed definitions, pipelines that heal themselves, and AI that answers from models rather than memory. On nights and weekends I ship commercial automation agents, which keeps me honest. Paying customers are the strictest code review there is.
Contact
Open to senior analytics and applied-AI IC roles.