Observability Operations Engineer

Cerebra; India; Hybrid; INR 1,100,000 - 1,900,000; Full time

Job summary

Cerebra is seeking an Observability Operations Engineer to implement and operate early-stage observability integrations across Kubernetes, Airflow, GCP, Databricks, and Azure. You will deploy Datadog agents, integrate Airflow health signals, and build Python/SQL checks for Datadog and Dynatrace, with a focus on telemetry quality and cost control.

The role requires 7+ years of experience and hands-on expertise across Kubernetes, Datadog, Airflow, and cloud environments.

Qualifications

• 7+ years of experience in observability, SRE, or related field.
• Hands-on with Kubernetes/GKE, Datadog and Dynatrace.
• Experience with Airflow, Python and SQL for checks and dashboards.
• Experience using Helm charts to deploy and manage workloads.
• Strong understanding of logs, metrics, traces, and observability tooling.

Responsibilities

• Deploy and manage Datadog Agent and Cluster Agent in Kubernetes/GKE using Helm and manifests.
• Integrate Airflow scheduler/workers DAGs and signals into Datadog and Dynatrace.
• Develop Python and SQL checks for system health, data freshness, and pipeline status.
• Configure metrics, logs, traces, dashboards, and monitors across environments.
• Validate telemetry and troubleshoot observability integrations.

Skills

Kubernetes/GKE deployment

Datadog/Dynatrace operations

Airflow monitoring/integration

Python

SQL

Helm charts

Telemetry monitoring

Tools

Kubernetes manifests

Job description

Immediate Joiners/Short Notice -Cerebra Payroll - Client MNC

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 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 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