Hybrid SRE Observability SLO Engineer

at GE Vernova

• Job title: SRE Observability SLO Engineer
• Job type: Full Time
• Experience level: Junior, Mid level
• Salary: Not specified
• Degree requirement: Bachelor's Degree
• Tech skills: AWS, Distributed Systems, Grafana, Kubernetes, Node.js, Prometheus, Python, Ray, Splunk
• Location requirements: Hybrid, Remote, United States

About the role

• SRE Observability SLO Engineer for GE Vernova’s GridOS Platform Engineering team. Building telemetry stack in SaaS reliability for critical energy infrastructure.

Responsibilities

• Implement organization-wide telemetry standards covering metrics, logs, and distributed traces across all GridOS SaaS services.
• Implement metrics collection for Kubernetes-hosted services (EKS/Rancher) including pod-level, namespace-level, and cluster-level metrics.
• Publish and maintain an Observability Runbook library covering onboarding, alert tuning, and dashboard standards for Platform SRE and Production DevOps teams.
• Partner with product engineering, Platform SRE, and customer stakeholders to define meaningful Service Level Indicators (SLIs) and Service Level Objectives (SLOs) per product and customer tier.
• Build and maintain SLO tooling — error budget burn-rate alerts, burn-rate dashboards, and automated SLO compliance reports.
• Design and build operational dashboards covering availability, latency, error rates, and saturation (the 'Golden Signals') for every GridOS SaaS product.
• Create executive-level dashboards for SRE leadership and customer-facing uptime/availability reports aligned to contractual SLAs.
• Conduct periodic observability health reviews to identify gaps in coverage, reduce MTTD (Mean Time to Detect), and improve MTTR (Mean Time to Resolve).

Requirements

• 2–3 years in SRE, observability engineering, or infrastructure reliability roles.
• Deep expertise with at least one major observability platform — Datadog, Grafana + Prometheus, AWS CloudWatch, Dynatrace, or New Relic.
• Hands-on experience implementing SLIs, SLOs, and error budget burn-rate alerting in a production SaaS environment.
• Strong understanding of distributed systems telemetry: metrics (Prometheus/CloudWatch), structured logging (CloudWatch Logs Insights, ELK), and distributed tracing (OpenTelemetry, AWS X-Ray).
• Experience with Kubernetes observability — kube-state-metrics, node exporters, Helm-deployed monitoring stacks, and namespace-level resource metrics.
• Proficiency in at least one query/visualization language: PromQL, Splunk SPL, Datadog Query Language, or CloudWatch Logs Insights query syntax.
• Experience designing alerting strategies that minimize alert fatigue through symptom-based and burn-rate approaches.
• Scripting skills in Python and/or Bash for automation of monitoring configuration and report generation.

Benefits

• Relocation Assistance Provided