About CircleCI Engineering

CI/CD has been solved. Now it's being reinvented. AI agents don't just run pipelines. They write code, trigger builds, interpret failures, and propose fixes. The interfaces that developers use to understand and trust that work don't exist yet. We're building them.

At CircleCI, our guiding principles aren't posters on a wall. We learn fast: we run experiments, accept failure, and scale what works. We think like the customer: every decision connects back to the developer sitting in front of a failing build at 2 am. We own the outcome: not just our slice of it, but the whole thing. If that's how you already work, you'll fit here.

About the Query Team

Every pipeline, job, and test that runs on CircleCI generates data, and that history is one of our biggest advantages in an agentic software delivery lifecycle. Flaky test detection, test insights, pipeline search, and agents that reason about why a build failed all depend on fast, reliable access to it.

The Query team owns the system that makes this possible. We pull product data out of the transactional services that create it and store it in datastores built for historical and analytical workloads. We serve it through domain-level APIs to CircleCI's product teams and to customers through our public API. Our goal is to make storing data "fire and forget" for every other team, and querying it fast and dependable at a scale that keeps growing.

What You'll Do

• Build AI-natively. AI is how we work. It's not a productivity layer on top of engineering, but the default starting point for how problems get approached and solved.
• Shape how we store data at scale. Own and harden our ClickHouse-based storage layer. Design schemas for known and emerging access patterns, and tune ingestion and query performance to support production-scale data.
• Build the pipelines that move our data. Design and own ingestion from event streams and durable workflows built on Temporal. Data should arrive reliably, stay queryable while it's in transit, and never slow down the critical path of the services that produce it.
• Serve data to the product and to customers. Evolve our platform into the default path for historical queries. Make getting historical data easy, not a bottleneck. Teams should be able to get what they need quickly and with minimal hand-holding, while Query keeps enough visibility into how the data is used to optimize ingestion and reads across every consumer. Build in the indexing, caching, and tiered storage required to keep it performant.
• Set technical direction. When the problem is complex or the answer isn't obvious, you lead the design discussion, write up the architecture decision, and make the call. You help decide which data domain we migrate next and how.
• Champion observability. Use Honeycomb, Datadog, and Rollbar to understand ingestion lag, query latency, and data correctness. For a data platform, operational data drives stability, not just incident response.
• Keep things moving. You take ambiguous data problems from design to production quickly, and you help the people around you move faster too.

You don't just patch problems, you ask why they happened. You'll fix the immediate issue, then solve the underlying team practice, process, or system that let it through.

What You'll Bring

• AI-native engineering practices. You use AI tools daily and can speak to what you've built, where they changed your approach, and where they fall short.
• Hands-on data experience. You've built or owned data-intensive systems such as ingestion pipelines, event-driven architectures, or analytical and time-series datastores. You understand the tradeoffs between transactional and analytical storage, and you've designed schemas and tuned queries for high-volume workloads. Experience with ClickHouse or another columnar database is a strong plus.
• Backend depth in Go. You're strong in Go, or strong in a comparable language and eager to work in Go: interface design, data modeling, service integration, and concurrency. Familiarity with React and TypeScript is a plus, since you'll occasionally work on the product surfaces that display this data.
• Distributed systems judgment. You understand delivery guarantees, idempotency, backpressure, and failure recovery. You know when to rely on a framework's reliability guarantees rather than building your own.
• Technical leadership. You've led the design of complex systems and made tradeoffs deliberately. You leave foundations others can build on. You raise the quality bar through reviews, mentoring, and direct feedback, not just through the code you write.
• A track record of shipping. 5+ years building and operating production backend systems. Your work has served real users, and you've treated latency, reliability, and correctness as design concerns.

Sound like you?

You care about how data powers the product, not just where it lives. You know how to make a datastore fast at scale, and have tuned schemas and queries against real production load. You think like a platform engineer, measuring your work by how much it unblocks the teams building on it. AI isn't a tool you reach for occasionally; it's a core part of how you approach and solve problems. If that sounds like you, we want to talk.