OBSERVABILITY ENGINEER

Job summary

The Observability Engineer role is responsible for managing the observability stack across over 50 production Kubernetes clusters, ensuring effective metrics, logging, tracing, and alerting for critical applications. Key responsibilities include designing and maintaining observability infrastructure using Prometheus, Grafana, and Loki, while also implementing AI/ML capabilities for intelligent monitoring and predictive alerting.

Qualifications

• Strong experience with enterprise observability platforms.
• Hands-on expertise with Prometheus, Grafana, Thanos, and Loki.
• Strong Kubernetes monitoring and observability experience.
• Experience with metrics collection, logging, alerting, and distributed tracing.
• Experience with GitOps practices and Infrastructure as Code.
• Strong understanding of Kubernetes architecture and containerized applications.
• Experience with multi-cluster and multi-environment observability.
• Knowledge of cloud object storage and cloud platforms.
• Strong understanding of metrics cardinality, storage optimization, and query performance.
• Strong troubleshooting, analytical, and problem-solving skills.

Responsibilities

• Design, deploy, and maintain enterprise-scale observability infrastructure using Prometheus, Grafana, Thanos, Loki, and modern collection agents.
• Manage observability deployments using GitOps principles and Infrastructure as Code.
• Implement long-term metrics storage solutions using cloud object storage.
• Maintain and upgrade observability components across Development, QA, UAT, Production, and DR environments.
• Configure distributed observability architectures across multiple data centers and cloud providers.
• Ensure observability platforms meet enterprise reliability, scalability, and performance requirements.
• Design and implement Prometheus monitoring strategies for Kubernetes infrastructure and containerized applications.
• Create ServiceMonitors and PodMonitors for automated metrics collection.
• Develop intelligent alerting rules while minimizing false positives.
• Configure multi-cluster metrics federation and aggregation.
• Optimize metrics cardinality, storage efficiency, and query performance.
• Monitor platform health and application performance across large-scale Kubernetes environments.
• Explore and implement AI/ML capabilities for intelligent monitoring and predictive alerting.
• Contribute to self-healing infrastructure and automated remediation capabilities.
• Identify opportunities to improve operational efficiency through automation and intelligent observability.

Skills

Relocation