Work Location

Pan India

Role SummaryThe 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 ResponsibilitiesDeploy, configure, upgrade, and troubleshoot Datadog Agent and Cluster Agent in Kubernetes/GKE using Helm and manifestsIntegrate Airflow scheduler, workers, DAGs, task runs, retries, duration, SLA, and failure signals into Datadog and/or DynatraceBuild Python and SQL checks/extractors for health, data freshness, pipeline status, utilization, and cost signalsConfigure Datadog and Dynatrace metrics, logs, traces, tags, dashboards,



and monitors; validate end-to-end telemetrySupport integrations across GCP, Databricks, and Azure, with attention to ingest volume, retention, and cost controlsPartner with SRE and the Datadog Administrator / Observability SME on incident workflows, runbooks, and deployment patternsDocument reusable integration and operations procedures for owning teams

Required SkillsLead: Kubernetes/GKE agent deployment, Datadog/Dynatrace operations, Airflow integrations, telemetry troubleshootingStrong: Python, SQL, Helm/manifests, logs, metrics, traces, API/integration development, monitoringWorking Knowledge: GCP(BigQuery, Dataflow, Airflow, Cloud Run, Cloud SQL, Redis, BigTable), Databricks, Azure, OpenTelemetry, cost/right-sizing signals, SLO and alerting concepts

📌 Observability Operations Engineer (Hyderabad)
🏢 Cerebra
📍 Hyderabad