Ticketing Platform Automation

Rebuilt the scraping infrastructure at Automatiq, the #1 automation platform in ticket resale, scaling from static EC2 instances to a distributed system processing 10M+ tickets daily.

As a Scraping Engineer at Automatiq — the #1 automation platform powering ticket resale, with $3.5B+ in priced inventory and 500+ customers — I rebuilt the company's core data acquisition infrastructure from the ground up.

I inherited a small, static fleet of EC2 instances running legacy scraping code and transformed it into a fully distributed system on AWS Fargate, processing over 10 million tickets daily across every major ticketing platform. Each provider was segmented into its own queue with independent autoscaling, which dramatically improved resource utilization and reduced time-to-resolution for customers.

The core challenge was a relentless cat-and-mouse game with Ticketmaster, SeatGeek, AXS, and other major platforms. These companies deployed increasingly sophisticated anti-automation measures — obfuscated JavaScript, browser fingerprinting, behavioral analysis, CAPTCHAs, and constantly rotating API signatures. Every few weeks, a platform would ship new countermeasures, and I'd reverse-engineer the updated JavaScript protections, deobfuscate their detection logic, and rebuild our integrations to stay ahead. This was a continuous cycle of breaking, patching, and adapting that ran for three years straight.

One notable result of this work was a "transferless" delivery mechanism — a method to deliver tickets to buyers without them ever leaving the seller's account, bypassing platform transfer restrictions entirely. I also built a serverless API that processed Apple Wallet ticket passes and converted them to distributable PDFs, unlocking a category of inventory that had previously been undeliverable.