System Monitoring and Observability

Design alert rules, on-call rotations, escalation policies, and runbooks that reduce noise, prevent alert fatigue, and ensure the right engineer gets paged for the right incident.

Analyze application performance using APM tools like Datadog, New Relic, Dynatrace, and Elastic APM. Identify bottlenecks, tune instrumentation, and optimize service health.

Design and implement distributed tracing systems using OpenTelemetry, Jaeger, Zipkin, and Tempo to track requests across microservices and pinpoint latency bottlenecks.

Design and build production-grade Grafana dashboards with PromQL, LogQL, and Tempo queries — covering SLO tracking, infrastructure overview, and service health panels.

Build complete observability for Kubernetes clusters — kube-state-metrics, cAdvisor, node exporters, pod log aggregation, and cluster health dashboards for platform teams.

Build and optimize log aggregation pipelines using Elasticsearch, Loki, OpenSearch, and Splunk. Write parsing rules, LogQL queries, and structured logging schemas for production systems.

Design scalable observability pipelines for metrics, logs, and traces using OpenTelemetry Collector, Fluentd, Vector, and Kafka to unify telemetry data at scale.

Design Prometheus metric schemas, write PromQL queries and recording rules, manage cardinality, and build scalable metrics infrastructure for cloud-native systems.

Define meaningful SLIs, SLOs, and error budgets aligned to user experience. Generate alerting rules, burn rate calculations, and reliability reporting for SRE teams.

Design synthetic monitoring checks, uptime tests, and user journey probes using Grafana Synthetic Monitoring, Checkly, Datadog Synthetics, and Blackbox Exporter.