Senior Site Reliability Engineer
United StatesEngineeringPosted
- Category
- Engineering
- Specialty
- Infra
- Level
- Senior
- Work arrangement
- Remote, some regions
- Employment
- Full-time
- Hiring in
- US
- Tags
- Python
- Go
- AWS
- Kubernetes
Job description
Responsibilities
-
Observability & Alerting: Design and improve monitoring, logging, tracing, dashboards, and SLI/SLOs for production visibility.
-
Automation & CI/CD: Build internal tools and delivery pipelines to boost efficiency, eliminate toil, and ensure reliable deployments.
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Reliability & System Quality: Drive continuous improvements in system performance, scalability, and security to mitigate customer impact.
-
Incident Management: Lead production incident response from triage to resolution, turning lessons into durable runbooks and preventatives.
-
Technical Leadership: Guide major technical initiatives, align cross-team engineering efforts, and manage technical risks.
-
Mentorship: Elevate SRE team capability through design reviews, paired problem-solving, and incident post-mortems.
-
Operations & On-Call: Join the on-call rotation while leading capacity planning and disaster recovery readiness.
-
AI/ML Operationalization: Leverage operational AI/ML tools to forecast capacity risks, detect patterns, and automate system health.
Qualifications
-
Experience: 8+ years in software/platform engineering or DevOps, including 5+ years dedicated to Site Reliability Engineering.
-
Infrastructure & Observability: Expertise in cloud platforms (AWS), Kubernetes, IaC, and full-stack observability (tracing, logging, SLI/SLOs). Grafana, Datadog, NewRelic
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Automation & Scripting: Proficient in Python, Go, or Bash for building CI/CD pipelines, production tools, and toil-reducing automation.
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Incident Leadership: Proven track record in root cause analysis, high-severity incident response, and long-term reliability engineering.
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Leadership & Mentorship: Strong communication skills with a history of mentoring engineers, leading cross-functional projects, and setting technical strategy.
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Operational AI/ML: Hands-on experience using AI/ML on telemetry data to predict capacity risks, spot anomalies, and optimize system workflows.
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