Datadog Implementation

Key Responsibilities: - Lead the end-to-end migration of observability systems from Azure Monitor to Datadog. - Design, build, and maintain Datadog dashboards, alerts, and SLO-based monitors. - Instrument applications and services to capture metrics, logs, and traces following best practices. - Implement and maintain logging infrastructure using Fluentd, with enhanced tagging and log enrichment. - Work with development and operations teams to ensure high availability, scalability, and reliability of services. - Implement infrastructure as code (IaC) for observability components using tools like Terraform. - Automate manual processes for monitoring, alerting, and environment setup using CI/CD pipelines.

Required Skills & Experience: - 48 years of combined experience in DevOps, SRE, or Observability Engineering roles. - Proven expertise in Datadog setup, configuration, APM instrumentation, and dashboarding. - Solid understanding and hands-on experience with Google Cloud Platform (GCP) services. - Experience in migrating observability platforms (preferably from Azure Monitor to Datadog or similar). - Strong experience with Fluentd, including log parsing, enrichment, and custom tagging. - Hands-on experience with CI/CD pipelines, preferably using GitLab CI, Jenkins, or GitHub Actions. - Proficiency with Infrastructure as Code tools (e.g., Terraform, Helm, Kustomize). - Good understanding of SRE practices: SLOs, SLIs, error budgets, and incident response. - Scripting skills in Python, Bash, or similar languages for automation. - Familiarity with containerized environments (e.g., Docker, Kubernetes). - Experience with advanced Datadog features such as: - Custom metrics and tags - Datadog Synthetic Monitoring - Real User Monitoring (RUM) - Log processing pipelines and processors - Watchdog and anomaly detection features - Service Map and Out-of-the-box dashboards - Datadog Notebooks for collaborative troubleshooting.