Site Reliability Engineer, Airbyte

• 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
• 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
• Ability & willingness to read backend code to understand how systems break and instrument them correctly
• 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
• If you find this role exciting, we encourage you to apply even if you think you don’t meet all of the requirements!

Desirable

• You'll be the infrastructure and reliability engineer on the Data Replication team - a full-stack product team running over 3 million sync jobs a week powering 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 through tooling
• You're equally comfortable in a Terraform file, a Kubernetes cluster, and a postmortem doc
• 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
• Own the infrastructure underpinning the Data Replication platform - Kubernetes clusters, CI/CD pipelines, secrets management, networking, and cloud resource configuration across AWS and GCP
• Partner with product engineers to reliably integrate product features with infrastructure
• Maintain and enhance observability, alerting, and anomaly detection with an eye towards LLM automation
• Maintain and enhance AI-augmented release and internal tooling: canary deployments, progressive rollouts, automated release qualification, and rollback automation - with an eye towards LLM automation
• Set the infrastructure bar for the team - build self-serve tooling, write runbooks, and coach engineers to own more of their stack