99 @dune dbt models. Hourly. Production-grade. Here's the honest version of how we got here. ➢ The beginning was not smooth. Weekend Slack alerts firing. Models broken. Me debugging incremental window logic at 11pm. Every incident got root-caused. Every root cause became a SOP rule. Slowly, the alerts stopped. Not because I got lucky — because the system learned. The turning point wasn't a single fix. It was patience + documentation. SOP v1 → v2.43. Every weekend alert became a permanent guardrail. Now migrations are genuinely smooth. The next model starts from a baseline that has already survived every failure mode we've hit. Big thanks to the @dune team — @kdotkrisp @onchain_ben @fr0zensun — for the technical reviews. They reviewed my incremental model designs, read through a very long SOP, and helped me think through the architecture properly. That kind of support matters. ➢ A thought on web2 vs web3 data work. At my previous big tech job, I had: • A fully managed internal data platform • A dedicated data engineering team to build the tables • Tooling that abstracted away almost everything I was a good analyst. But I was operating inside a very comfortable box. In web3, none of that exists. I own the full stack: upstream event indexing → dbt models → incremental design → CI validation → dashboard cutover. It's harder. But I enjoy it more. Because now I understand the entire data supply chain — not just the last mile. And an analyst who understands the full stack is significantly harder to make obsolete. That's what I mean by antifragile. The friction was the point.