I build production APIs, data pipelines, queues, and operational systems with TypeScript/Node.js, Python, PostgreSQL, and Redis. I started in warehouse operations and learned to code by replacing slow, error-prone workflows with software people could use every day.
My work includes the sign-in, payments, and usage-credit system behind an AI product (client work, hired twice), a pricing platform spanning 15 states, 1,000+ locations, 175,000+ products, and 13M+ historical price points, and warehouse software in daily production since December 2024.
Open to backend roles (remote, US) and to fixed-scope contract work. Five client reviews at claygeo.dev.
Email · Portfolio · LinkedIn · X
Payments and usage credits for an AI product: client work, two contracts
Clerk sign-in, Stripe Checkout, and a usage-credit ledger for a product that runs long, expensive generation jobs. A webhook delivered twice cannot grant credits twice, two requests cannot both spend the last credit, and a run that fails to deliver never costs the user a credit: a credit is held when a run starts, settled when the deliverable exists, and released by an inactivity timer if the run is abandoned. The client read the ledger code and ran the test suite before releasing each milestone, then hired me a second time to take the product to production: nginx with auto-renewed TLS, the app under systemd, a managed Postgres that is not reachable from the internet with separate owner and runtime roles, and production Stripe with the key's permissions cut to what the app uses. Both contracts ran entirely in writing.
Competitive Intelligence Platform: production architecture case study
The data pipeline behind competitive pricing at a multi-state retailer. BullMQ and Redis workers ingest and normalize 175,000+ products and 13M+ historical price points from 1,000+ locations into PostgreSQL, with OCR, deduplication, retries, and automated recovery. It replaced roughly two weeks of manual work per collection cycle and is used daily by the pricing team.
Warehouse Labeling System: production architecture case study
Browser-based warehouse software that converts inconsistent CSV and Excel exports into scannable Zebra thermal labels. Operators scan a barcode, resolve the product, assign a batch, and print product, date, allergen, and box data. A three-tier catalog cache keeps the workflow available through network drops. It has run in daily production since December 2024.
A Rust service that subscribes to live Solana swaps on two exchanges over a WebSocket, parses them on a bounded worker pool so a busy slot slows the parsers and never the socket reader, and flags front-run, victim, back-run triples within 3 slots. Idempotent Postgres writes, a Server-Sent Events feed, and a backtest binary with a precision and recall gate that must pass before it goes live.
- Payments, webhooks, and ledgers that reconcile
- Backend services and REST APIs
- PostgreSQL data modeling and transactional workflows
- Queues, workers, retries, and failure recovery
- Real-time WebSocket systems
- Messy third-party integrations and browser automation
- CI, observability, and operational handoff
Core stack: TypeScript · Node.js · Python · PostgreSQL · Redis · Stripe · BullMQ · WebSockets · Supabase · Docker · Playwright