Site Reliability Engineer Roadmap

0

Prerequisites (CRITICAL Foundation)

Duration: 3-5 years of experience required

Essential Experience Before SRE

Required Background:

Software Engineering: Proficiency in at least one language (Python, Go, Java), data structures and algorithms, system design basics, debugging and troubleshooting

Operations Experience: 2-3+ years as DevOps Engineer, SysAdmin or Cloud Engineer. Production system management, incident response, on-call experience

Infrastructure: Linux administration, cloud platforms (AWS/GCP/Azure), containers and Kubernetes, networking fundamentals, distributed systems basics

DevOps Skills: CI/CD pipelines, infrastructure as code, monitoring and logging

Reality Check:

SRE is NOT an entry-level role. Most companies require 3-5 years of relevant experience. Complete DevOps Engineer or Cloud Engineer roadmap first, work in production environments, then transition to SRE. This roadmap assumes you have the foundation.

1

SRE Principles & Philosophy

Duration: 4-6 weeks

Core SRE Concepts

What to Learn:

SRE vs DevOps vs traditional ops, the 50/50 rule (50% engineering, 50% ops), eliminating toil (repetitive manual work), embracing risk and error budgets, service level terminology (SLI, SLO, SLA), the SRE workday structure, Google's SRE principles and practices

Free Resources:

Site Reliability Engineering book (Google, free online)
The Site Reliability Workbook (Google, free online)
Building Secure and Reliable Systems (free online)

Key Takeaways:

Understand that SRE is engineering-focused, reliability is a feature not an afterthought, 100% reliability is the wrong target, systematic problem-solving over heroics

Service Level Objectives (SLOs)

What to Learn:

Defining Service Level Indicators (SLIs) - latency, availability, throughput, correctness. Setting Service Level Objectives (SLOs) - realistic targets based on user happiness. Service Level Agreements (SLAs) - business contracts with consequences. Error budgets - how much failure is acceptable, error budget policies

Free Resources:

Implementing SLOs (Google SRE book chapter)
SLO workshop materials
Error budget policy examples

Hands-On Practice:

Define SLIs for sample services, set SLOs with business justification, calculate error budgets, create error budget policy

Eliminating Toil

What to Learn:

Defining toil (manual, repetitive, automatable, tactical, no enduring value), measuring toil in your work, calculating toil vs engineering work ratio, prioritizing toil elimination, automation strategies, building self-service tools

Free Resources:

Eliminating Toil (SRE book chapter)
Toil measurement framework
Automation case studies

Hands-On Practice:

Audit your current work for toil, identify automation opportunities, build tools to eliminate repetitive tasks, measure time saved

2

Monitoring, Alerting & Observability

Duration: 8-10 weeks

SRE-Style Monitoring

What to Learn:

Four golden signals (latency, traffic, errors, saturation), whitebox vs blackbox monitoring, symptom-based monitoring vs cause-based, monitoring distributed systems, time-series databases (Prometheus, VictoriaMetrics), exporters and instrumentation, recording rules and aggregation

Free Resources:

Monitoring Distributed Systems (SRE book)
Prometheus documentation
Grafana best practices

Hands-On Practice:

Instrument services with four golden signals, build Prometheus monitoring stack, create dashboards aligned with SLOs, implement custom exporters

Effective Alerting

What to Learn:

Principles of good alerts (urgent, actionable, user-impacting), alert fatigue and how to prevent it, symptom-based vs cause-based alerts, alert routing and escalation, on-call best practices, pages vs tickets, alert tuning and maintenance, alert design workshop

Free Resources:

Practical Alerting from Time-Series Data
My Philosophy on Alerting (Rob Ewaschuk)
Alertmanager documentation

Hands-On Practice:

Design alert rules for services, configure Alertmanager routing, audit existing alerts for quality, implement alert SLOs

Distributed Tracing & Deep Observability

What to Learn:

Three pillars of observability (metrics, logs, traces), distributed tracing concepts, OpenTelemetry instrumentation, Jaeger/Tempo/Zipkin, trace sampling strategies, correlating metrics, logs and traces, debugging production issues with tracing

Free Resources:

OpenTelemetry documentation
Distributed tracing guide
Observability Engineering book (excerpts)

Hands-On Practice:

Implement distributed tracing with OpenTelemetry, visualize traces in Jaeger, debug slow requests using traces, build trace-based alerts

3

Incident Management & On-Call

Duration: 6-8 weeks

Incident Response

What to Learn:

Incident response lifecycle, roles (incident commander, communications lead, ops lead), incident severity levels, escalation procedures, war rooms and coordination, incident documentation during crisis, when to declare an incident, incident command system (ICS)

Free Resources:

Managing Incidents (SRE book)
Incident.io handbook
PagerDuty Incident Response guide

Hands-On Practice:

Practice incident response drills (game days), role-play as incident commander, write incident reports, conduct tabletop exercises

Postmortems & Learning from Failure

What to Learn:

Blameless postmortem culture, postmortem template and structure, root cause analysis (5 whys, fishbone diagrams), contributing factors vs root causes, action items and follow-through, sharing lessons learned, postmortem review process

Free Resources:

Postmortem Culture (SRE book)
Blameless postmortem examples
Etsy Debriefing Facilitation Guide

Hands-On Practice:

Write postmortems for past incidents, practice blameless analysis, facilitate postmortem reviews, track action item completion

On-Call Best Practices

What to Learn:

On-call rotation schedules, on-call handoff process, escalation policies, managing on-call burden and burnout, compensation and work-life balance, runbooks and playbooks, on-call readiness reviews, being a good on-call engineer

Free Resources:

Being On-Call (SRE book)
Effective On-Call guide
PagerDuty On-Call best practices

Hands-On Practice:

Shadow on-call engineers, participate in on-call rotations, improve runbooks during on-call, measure on-call quality metrics

4

Reliability Engineering & Scalability

Duration: 10-12 weeks

Distributed Systems Reliability

What to Learn:

CAP theorem and trade-offs, consistency models (strong, eventual, causal), failure modes in distributed systems, cascading failures and how to prevent them, circuit breakers and bulkheads, retry policies and exponential backoff, rate limiting and load shedding, graceful degradation

Free Resources:

Designing Data-Intensive Applications (excerpts)
Distributed systems lectures (MIT 6.824)
AWS Architecture Center (patterns)

Hands-On Practice:

Design systems with failure in mind, implement circuit breakers, test cascading failure scenarios, practice failure mode analysis

Capacity Planning

What to Learn:

Forecasting demand (organic growth, launches, seasonality), resource utilization metrics, capacity modeling and simulation, provisioning lead time, buffer capacity, load testing and stress testing, cost optimization vs capacity, service headroom and safety margins

Free Resources:

Managing Critical State (SRE workbook)
Capacity planning guide
Load testing best practices

Hands-On Practice:

Forecast capacity needs, perform load testing, create capacity models, present capacity recommendations with business justification

Performance Engineering

What to Learn:

Performance profiling (CPU, memory, I/O), identifying bottlenecks, database query optimization, caching strategies (CDN, application, database), load balancing and traffic management, horizontal vs vertical scaling, latency percentiles (p50, p95, p99), performance regression detection

Free Resources:

Systems Performance (Brendan Gregg blog)
Performance analysis toolkit
Database optimization guides

Hands-On Practice:

Profile applications for bottlenecks, optimize slow queries, implement caching, conduct performance testing, measure improvements

Chaos Engineering

What to Learn:

Chaos engineering principles, designing chaos experiments, blast radius and safety, failure injection (network, compute, dependencies), observing system behavior under failure, game days and disaster recovery drills, Chaos Monkey and similar tools, building confidence in production

Free Resources:

Principles of Chaos Engineering
Chaos Monkey documentation
Game day exercises guide

Hands-On Practice:

Design chaos experiments, inject failures in test environments, conduct game days, document learnings and improvements

5

Automation & Software Engineering

Duration: 8-10 weeks

SRE Tooling & Automation

What to Learn:

Building production-grade tools, automation frameworks, API development for operations, self-service platforms, workflow orchestration, configuration management at scale, testing automation tools, documentation and usability, tool lifecycle management

Free Resources:

Automation at Google (SRE book)
Building internal tools guide
Platform engineering resources

Hands-On Practice:

Build CLI tools for common tasks, create web interfaces for operations, automate complex workflows, gather user feedback and iterate

Release Engineering

What to Learn:

Progressive delivery (canary, blue-green, feature flags), release automation, deployment strategies and rollback procedures, release validation and testing, change management, release coordination, automated rollback triggers, measuring deployment success (DORA metrics)

Free Resources:

Release Engineering (SRE book)
Progressive delivery patterns
Feature flag best practices

Hands-On Practice:

Implement canary deployments, build automated rollback systems, use feature flags for gradual rollouts, measure deployment metrics

Data Processing & Analysis

What to Learn:

Time-series data analysis, statistical analysis for reliability, anomaly detection, log analysis at scale, building data pipelines for metrics, querying observability data, creating reliability dashboards, data-driven decision making

Free Resources:

PromQL guide (Prometheus query language)
Statistics for engineers
Log analysis techniques

Hands-On Practice:

Write complex PromQL queries, analyze service metrics, detect anomalies, build automated analysis tools

6

Portfolio, Interviews & Career Growth

Duration: Ongoing

Building Your SRE Portfolio

What to Showcase:

Automation tools you've built, SLO implementations, incident postmortems (sanitized), monitoring and alerting solutions, reliability improvements with measurable impact, technical blog posts on SRE topics, open source contributions, speaking at meetups/conferences

Portfolio Projects:

Production-grade monitoring stack
Chaos engineering framework
SLO tracking and alerting platform
Incident management automation
Capacity planning tooling

SRE Interview Preparation

What to Prepare:

System design for reliability, incident response scenarios, SLO discussions, troubleshooting and debugging, coding challenges (algorithms, data structures), behavioral questions (STAR method), discussing past incidents and learnings, architecture trade-offs

Common Questions:

"Design a system for 99.99% availability"
"Tell me about a production outage you resolved"
"How do you measure and improve reliability?"
"Design monitoring for a distributed system"
"What's your approach to on-call and incidents?"

Continuous Learning & Growth

Growth Areas:

Stay current with industry practices, read postmortems from other companies, attend SREcon conferences (free recordings), participate in SRE communities, mentor others, write about your learnings, contribute to open source SRE tools, deep dive into specific domains (security, ML systems, databases)

Certifications:

While not required, consider: CKA (Kubernetes Administrator), AWS/Azure/GCP Professional certifications, focuses more on experience and projects than certs

What is Site Reliability Engineering?

Key Facts

Career Progression Path

Associate SRE / Junior SRE