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Observability Operations Engineer
Experience: 7+ Years
Work Location: Pan India
Project Duration: 12 Months
Joining: Immediate/Short Notice

Role Summary: The Observability Operations Engineer will implement and operate early-stage observability integrations across Kubernetes, Airflow, GCP, Databricks, and Azure environments. This role deploys Datadog agents, integrates Airflow health and pipeline signals, and builds practical Python and SQL checks for Datadog and Dynatrace, with robust attention to telemetry quality, ingest volume, retention, and cost.

Key Responsibilities

- Deploy, configure, upgrade, and troubleshoot Datadog Agent and Cluster Agent in Kubernetes/GKE using Helm and manifests.
- Integrate Airflow scheduler, workers, DAGs, task runs, retries, duration, SLA, and failure signals into Datadog and/or Dynatrace.
- Develop Python and SQL checks for system health,



data freshness, pipeline status, resource utilization, and cost monitoring.
- Configure and manage metrics, logs, traces, tags, dashboards, and monitors.
- Validate end-to-end telemetry and troubleshoot observability integrations.
- Support integrations across GCP, Databricks, and Azure environments.
- Monitor telemetry ingestion volume, retention, and cost controls.
- Collaborate with SRE and observability teams on incident workflows, runbooks, and deployment procedures.
- Document reusable integration and operational procedures.

Required Skills

- Kubernetes/GKE and agent deployment
- Datadog and/or Dynatrace operations
- Apache Airflow monitoring and integrations
- Python and SQL
- Helm charts and Kubernetes manifests
- Logs, metrics, traces, APIs, and integration development
- Monitoring, telemetry troubleshooting, and alerting

📌 Observability Operations Engineer (India)
🏢 Cerebra
📍 India