Principal Engineer - Data

For twenty years, "using the data" meant a person opening a dashboard and clicking until the number showed up. That's ending. The thing asking the question now is often an agent — a salesperson's AI fetching its own answer, a developer's agent interrogating build history from inside the product. The dashboard was the interface built for humans. What comes next has to work for machines and humans at once, and almost no one has architected a data platform for it. You'd be the one who does it here. 🔧 The problem you'd own Buildkite's data goes deep — the build and test history behind how teams like Airbnb, OpenAI, Canva, Anthropic and Pinterest ship software. Storing it was never the hard part. The hard part is the question underneath everything now: can an agent find it, trust it, and act on it — safely, and fast enough to matter? Answering that means rebuilding from the query engine up. What an agent actually needs from a semantic layer. What "self-serve" means when the person asking isn't technical. Where the current architecture has to bend to make it possible — starting with a live call on moving from Athena to ClickHouse, which you'd be the one to settle. And the surface is wide: the same platform has to answer a GTM teammate typing a plain-English question and a customer's agent querying build data inside the product. Internal and external, off one foundation. This is a principal-level role that sets the direction on how this gets solved. 🚀 The work itself You'd own data architecture at Buildkite: the strategy, the technical calls, and the patterns the rest of the data effort builds on. Most senior data practitioner in the company, and the advocate for data across engineering. You'd choose the stack rather than inherit it — query engines, semantic layer, ingestion and streaming, storage — and stand behind the choices. You'd design for agents and humans both, so data is discoverable, trustworthy, and safe to act on. And you'd stay hands-on where it counts, chas