Staff Site Reliability Engineer

Replit Replit · Enterprise · EUROPE · Remote · Engineering

Staff Site Reliability Engineer role focused on ensuring the reliability, scalability, and performance of Replit's infrastructure. Responsibilities include architecting observability solutions, defining reliability standards, leading incident response, driving automation, optimizing performance on Kubernetes, debugging distributed systems, and mentoring engineers. Requires strong programming skills in Python/Go, deep understanding of distributed systems, Kubernetes, and observability tools.

What you'd actually do

  1. Architect and Implement Observability: Design, build, and lead the implementation of comprehensive monitoring, logging, and tracing solutions. Create dashboards and metrics that provide real-time visibility into system health and performance, enabling proactive issue detection.
  2. Define and Drive Reliability Standards: Work with product and engineering teams to define, implement, and track Service Level Objectives (SLOs) and Service Level Indicators (SLIs). Build systems to monitor and report on these metrics, holding teams accountable and ensuring we maintain high reliability standards while balancing innovation speed.
  3. Lead Incident Management and Response: Act as a senior leader during high-impact incidents, guiding the team to rapid resolution. Conduct thorough, blameless post-mortems and drive the implementation of preventative measures. Develop and refine runbooks and build automation to reduce Mean Time To Recovery (MTTR).
  4. Drive Automation and Infrastructure as Code: Architect, build, and improve automation to eliminate toil and operational work. Design and maintain CI/CD pipelines and infrastructure automation using tools like Terraform or Pulumi. Create self-healing systems that can automatically respond to common failure scenarios.
  5. Optimize Performance on Kubernetes: Collaborate with core infrastructure and product teams to performance-tune and optimize our large-scale cloud deployments, with a deep focus on Kubernetes, Docker, and GCP. Identify and resolve performance bottlenecks, implement capacity planning strategies, and reduce latency across global regions.

Skills

Required

  • 8-10 years of experience in Site Reliability Engineering or similar roles (e.g., DevOps, Systems Engineering, Infrastructure Engineering).
  • Strong programming skills in languages like Python or Go. You write high-quality, well-tested code.
  • Deep understanding of distributed systems. You’ve designed, built, scaled, and maintained production services and know how to compose a service-oriented architecture.
  • Deep experience with container orchestration platforms, specifically Kubernetes, and cloud-native technologies.
  • Proven track record of designing, implementing, and maintaining sophisticated monitoring and observability solutions (e.g., metrics, logging, tracing).
  • Strong incident management skills with extensive experience leading incident response for complex systems and demonstrated critical thinking under pressure.
  • Experience with infrastructure as code (e.g., Terraform, Pulumi) and configuration management tools.
  • Excellent written and verbal communication skills, with an ability to explain complex technical concepts clearly and simply and a bias toward open, transparent cultural practices.
  • Strong interpersonal skills, with experience working with and mentoring engineers from junior to principal levels.
  • A willingness to dive into understanding, debugging, and improving any layer of the stack.

Nice to have

  • Deep experience with Google Cloud Platform (GCP) services and tools.
  • Expert-level knowledge of modern observability platforms (e.g., Prometheus, Grafana, Datadog, OpenTelemetry).
  • Experience designing and building reliable systems capable of handling high throughput and low latency.
  • Significant experience with Go and Terraform.
  • Familiarity with working in rapid-growth, startup environments.
  • Experience writing company-facing blog posts and training materials.

What the JD emphasized

  • Kubernetes
  • monitoring and observability
  • incident management