Observability Operations Engineer

Cerebra; Hyderabad, Chennai, Bengaluru; Hybrid; INR 1,500,000 - 2,300,000; Full time

Job summary

Cerebra is seeking an Observability Operations Engineer to implement and operate observability integrations across Kubernetes, Airflow, GCP, Databricks, and Azure environments. The role focuses on deploying Datadog agents, integrating Airflow health signals, and building Python/SQL checks to ensure telemetry quality, ingestion control, and cost efficiency.

You will collaborate with SRE to define incident workflows, runbooks, and deployment patterns, while documenting procedures for owning teams

Qualifications

• Implement observability integrations across Kubernetes, Airflow, GCP, Databricks, and Azure environments.
• Deploy Datadog agents and integrate Airflow health and pipeline signals into monitoring.
• Build Python and SQL checks for telemetry quality, ingestion, retention, and cost signals.
• Collaborate with SRE on incident workflows, runbooks, and deployment patterns.

Responsibilities

• Deploy, configure, upgrade, and troubleshoot Datadog Agent and Cluster Agent in Kubernetes/GKE using Helm and manifests.
• Document reusable integration and operations procedures for owning teams.

Skills

Kubernetes deployment

Datadog operations

Dynatrace operations

Airflow integrations

Telemetry troubleshooting

Python

SQL

Helm/manifests

Logs & metrics

Tools

Kubernetes

GKE

Datadog

Dynatrace

Airflow

OpenTelemetry

BigQuery

Dataflow

Cloud Run

Job description

Work Location

Pan India

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 strong 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 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 Skills

• Lead: 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