Senior Site Reliability Engineer
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About Airbyte:We’re on a mission to make data available and actionable to everyone, everywhere. With 700+ connectors, our platform powers analytics and gives AI agents the business context they need to do real work. More than 25,000 companies, including 20% of the Fortune 500, rely on Airbyte to put their data to work.
We’re built on open source because we believe companies should have full control over their data. A community of 27K members helps shape what we build, and we’re backed by $181M from investors including Benchmark, Accel, Altimeter, Coatue, and Y Combinator.
Models keep getting better. What holds AI back is the data informing and shaping its decisions, and that's the layer we're building.
We’re just getting started. If you’re excited by big challenges, meaningful ownership, and the chance to help build what’s next, we’d love to meet you.
The Role:You'll be one of two infrastructure and reliability specialists on the Data Replication team, a full-stack product team running millions of sync jobs a week that power thousands of data use cases across multiple regions and clouds. You'll build and maintain the infrastructure, set reliability standards, drive down incidents, and make it easier and safer for engineers to ship.
Expect to spend roughly 30 to 50% of your time in backend application code, and the rest on infrastructure and reliability. When the fix belongs in the application, you'll ship it there yourself, and you'll build features alongside product engineers. We believe every abstraction leaks, so the best engineers work up and down the stack. You're equally comfortable in a Terraform file, a Kubernetes cluster, a service's codebase, and a postmortem doc. And with two of you in the room, we want someone who will challenge a design, not just implement it.
We expect engineers here to actively use AI as a force multiplier: agentic tools to automate toil, augment incident response, and build smarter internal tooling. If you're not already doing this, you should be excited to start. We care as much about how you work as what you build. Trust, directness, and craftsmanship matter here.
What You’ll Do:- Own the infrastructure underpinning the Data Replication platform: Kubernetes clusters, CI/CD pipelines, secrets management, networking, and cloud resource configuration across AWS and GCP.
- Work across the stack: ship backend features and fixes in application code alongside product engineers, not just the infrastructure underneath them.
- Improve performance and efficiency at scale, from bursty job scheduling to right-sizing compute.
- Help us ship safely into customer-managed environments, where Airbyte runs in infrastructure we don't control.
- Evolve observability, alerting, and anomaly detection, with an eye toward LLM automation.
- Build AI-augmented release and internal tooling: canary deployments, progressive rollouts, automated release qualification, and rollback automation.
- Set the infrastructure bar for the team: build self-serve tooling, write runbooks, and coach engineers to own more of their stack.
What You’ll Need:- 7+ years in infrastructure, platform engineering, SRE, or DevOps.
- Hands-on ownership of Kubernetes, Helm, and Terraform in production environments.
- Deep experience with observability stacks (Prometheus, Grafana, Datadog) and on-call operations.
- Experience with CI/CD pipeline ownership and developer tooling.
- Experience shipping changes to backend services in [language], not only infrastructure code, and the appetite to do more of it.
- Strong design judgment, and the confidence to challenge infrastructure decisions with data.
- Fluency with AI tools: LLMs and agentic frameworks to automate, debug faster, and reduce toil.
- A startup-ready mindset: comfortable with ambiguity, moving fast, and owning problems end to end.
Nice To Have:- Data pipelines, replication systems, or ETL/ELT platforms.
- Control plane / data plane architectures...
Apply now
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About Airbyte
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Open-source data integration platform with 700+ connectors that moves company data into analytics and AI systems.
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