Own system reliability through SLOs, error budgets, observability engineering, incident response, and chaos experiments. Keep production alive and healthy.
SREs apply software engineering principles to infrastructure and operations problems. They design for reliability from the start and own the production health of services.
SRE originated at Google and has since become a standard role in large-scale technology organizations. It sits at the intersection of operations and software engineering.
SREs are typically the most technically senior operations-adjacent engineers. They often have a coding background and apply it to automation, tooling, and reliability systems.
Build from Linux and containers through to advanced observability, reliability engineering, and AI-assisted operations.
Deep Linux knowledge is non-negotiable for SREs. Master processes, networking, storage, systemd, and production debugging at the OS level.
Understand how applications are packaged and run in containers — critical for diagnosing runtime failures in containerized environments.
Master Kubernetes operations: scheduling, resource limits, liveness probes, HPA, PodDisruptionBudgets — tools SREs use to ensure reliability.
Operate Kubernetes on Azure: node pool management, cluster upgrades, networking, monitoring integration, and production scaling patterns.
Instrument services, define SLI metrics with PromQL, set up recording rules, and configure Alertmanager for SLO-breach notifications.
Build SLO dashboards, error budget burn-rate charts, and incident response dashboards that give real-time visibility into production health.
Deep APM with Dynatrace — distributed tracing, Davis AI anomaly detection, full-stack visibility, and automated root cause analysis for incidents.
Master SLIs, SLOs, error budgets, toil measurement, incident response frameworks, postmortem culture, and chaos engineering principles.
Understand Helm release lifecycle to diagnose deployment failures, manage rollbacks, and participate in production readiness reviews for new services.
Apply AI to SRE workflows: log anomaly detection, incident summarization, alert noise reduction, and self-healing infrastructure patterns.
Define availability and latency SLOs for 3 critical services, build Grafana dashboards showing live error budget consumption and burn rate, and configure alerts for budget exhaustion.
Build a runbook-driven response system: Prometheus alert → Alertmanager → PagerDuty → automated diagnostic script run → Slack summary. Reduce MTTR from 45 minutes to 8 minutes.
Use chaos tools to inject pod failures, network latency, and node termination into staging, measure how services respond, and use the findings to improve resilience before production incidents occur.