• Implemented DataDog APM, logging, and metrics to achieve end-to-end observability across microservices and Kubernetes workloads.

• Built custom dashboards and monitors in DataDog for performance metrics, SLO tracking, and anomaly detection, reducing mean time to resolution (MTTR) by 30%.

• Configured synthetic monitoring and real user monitoring (RUM) in DataDog for critical services, improving customer experience visibility.

• Automated alerts and incident workflows using DataDog integrations with PagerDuty and Slack, accelerating incident response.

• Integrated DataDog monitoring with CI/CD pipelines (Jenkins, GitHub Actions) to validate system health post-deployment.

• Migrated observability from Prometheus/Grafana to DataDog, centralizing monitoring and reducing tool sprawl.

• Automated infrastructure provisioning with Terraform and Ansible for AWS/Azure resources, ensuring consistent deployments.

• Managed Kubernetes clusters (EKS/AKS) with observability enabled through DataDog Operators and Kubernetes event collection.

• Provided L3 support for monitoring and logging pipelines, leveraging DataDog log processing pipelines to improve visibility into distributed systems.

• Deployed DataDog dashboards and alerts for Elasticsearch clusters, improving proactive incident detection.

• Managed AWS EKS clusters with integrated DataDog Kubernetes monitoring for pod health, cluster metrics, and network latency.

• Implemented DataDog log ingestion pipelines to centralize application logs with structured parsing.

• Built CI/CD pipelines with Jenkins, integrated with DataDog CI Visibility to monitor pipeline performance and failures.

• Collaborated with developers to adopt observability-driven development, embedding DataDog APM instrumentation in services.

• Configured DataDog and CloudWatch monitoring for hybrid Elasticsearch clusters deployed across AWS and Azure.

• Designed CI/CD pipelines for observability deployment automation.

• Implemented DataDog monitors for log anomalies in financial and healthcare applications, reducing downtime

• Deployed SQL Server monitoring in DataDog and Azure SQL Database, creating dashboards for performance and query insights.

• Automated backup, recovery, and performance alerts through integrated monitoring systems.

• Improved data reliability and high availability with DataDog + Azure Monitor integration