DevOps and SRE Roadmap
From Linux fundamentals to production-grade infrastructure. Master containers, orchestration, CI/CD, monitoring, and cloud platforms.
$100K-$200K (US) / $50K-$110K (remote global)
9-15 months of focused learning
15,000+ monthly US job postings. Fastest growing engineering discipline at 15% annual growth. Highest premiums for Kubernetes and cloud expertise.
Market data sources
›Market overview
DevOps bridges development and operations. The field has evolved from "ops guys who script" to platform engineering, SRE, and cloud-native infrastructure. Automation, observability, and reliability are the core concerns. The best DevOps engineers understand both the developer experience and the operational reality.
The 10,000-hour rule says mastery requires roughly that many hours of deliberate practice. At 1% improvement per day, you are 37x better in a year. This roadmap is a structured path, not a race: follow the steps in order, build the projects, and trust the process.
Master the Linux CLI, shell scripting, and package every service in Docker with optimized, secure images and docker-compose for local dev.
Deploy apps on Kubernetes with Helm, probes, and autoscaling. Ship a CI/CD pipeline with parallel jobs, quality gates, and approval steps.
Provision infrastructure with Terraform modules and remote state. Deploy a Prometheus + Grafana stack with SLOs and alerting rules.
Architect on a cloud platform with IAM least-privilege, cost controls, and multi-region failover. Harden the pipeline with vulnerability scanning.
AWS vs GCP vs Azure
Most companies standardize on a single cloud. The choice affects your daily tooling, certification path, and the kind of infra problems you will own.
| Aspect | Option A | Option B | Option C |
|---|---|---|---|
| Market Share | Largest (~32%); default for most startups | Strong in data/ML (BigQuery, Vertex AI) | Dominates enterprise (Office 365, AD, Azure AD) |
| Core Strengths | Broadest service catalog; S3, Lambda, EKS | Best data/ML; Kubernetes-native (GKE) | Enterprise integration, hybrid cloud |
| Certifications | SAA / SA Pro: most recognized | ACE / PCA: respected in data roles | AZ-104 / AZ-305: valued in enterprise |
| Learning Curve | Steep (enormous catalog, deep docs) | Moderate (simpler, cleaner console) | Moderate (portal heavy, similar concepts) |
| Community | Largest; most tutorials and hiring demand | Great docs; smaller hiring pool | Huge enterprise community |
Prometheus vs Datadog vs ELK
Observability stacks differ in cost, flexibility, and operational burden. Open source gives control; SaaS gives speed to value.
| Aspect | Option A | Option B | Option C |
|---|---|---|---|
| Cost | Free self-hosted; SRE time is the cost | Expensive at scale (per-host + per-metric) | Free OSS; ES infra cost grows fast |
| Metrics | Best-in-class pull model + Alertmanager | Excellent out-of-box dashboards | Weak for metrics (focused on logs) |
| Logs | Loki pairs well; separate system | Native log management integrated | Logs are its core strength |
| Tracing | Pairs with Tempo/Jaeger (OpenTelemetry) | APM included with trace explorer | APM is separate (Elastic APM) |
| Best For | Kubernetes-native teams wanting control | Teams that want everything managed | Log-heavy compliance workloads |
Cloud Platform Engineering Path
Go deep on one cloud provider: networking, IAM, serverless, and cost optimization. Ideal for platform engineering roles.
SRE Path
Focus on reliability engineering: SLOs, error budgets, incident response, and chaos engineering for high-availability systems.
Security / DevSecOps Path
Shift security left: SAST/DAST in CI, secrets management, compliance automation, and zero-trust networking.