Roadmaps
Learning paths by role
Backend, AI, Data, DevOps, and Staff/Principal — prerequisites and what to do next.
These paths are interview roadmaps, not documentation tours. Each stop links to lab questions and book chapters. Do the labs aloud on a timer.
Backend Engineer
- Coding patterns: Coding Lab Q1–Q20 (timed).
- Fundamentals: Scaling, Caching, SQL/NoSQL, Queues.
- Design drills: System Design Lab Q1, Q3, Q4, Q7, Q9, Q13, Q16.
- Comparisons: Kafka vs RabbitMQ, Redis vs Memcached, REST vs GraphQL.
- Behavioral: STAR + leadership stories.
AI Engineer
- Core book: LLMs → RAG → Agents → MCP.
- Lab: AI Lab all Q1–Q17 (especially Q1, Q3, Q4, Q11, Q12, Q16).
- Serving & cost: Q8, Q11, Q13.
- Eval & shipping: Q3, Q17.
- Optional coding: sliding window + graphs for general screens.
Data Engineer
- Queues & logs: Queues, SD Lab Q16 Kafka.
- Storage: SQL/NoSQL, Replication, SD KV store.
- Batch/stream tradeoffs; feature store AI Lab Q15.
- Coding: arrays, heaps, sorting intervals for screens.
DevOps / Platform
- Scaling & LB: Scaling, rate limiter SD Q4.
- Caching CDN: Caching, SD Q9.
- Observability, degradations, backpressure (SD scaling drills).
- AI gateway quotas if platform-for-AI: AI Lab Q8, Q11.
Staff / Principal Engineer
- Every SD answer must include failure modes, org interfaces, and migration plans.
- Deep labs: payments Q13, KV Q15, feed Q2, video Q6, RAG platform Q1+Q8+Q11.
- Write comparison tradeoffs from Comparisons without notes.
- Behavioral: conflict, influence without authority, technical strategy — Behavioral lab.
- Teach: explain a design to a junior in 10 minutes (interviewers test this).
How to use levels on each question
Open any Interview Lab card: use Level expectations to self-score. Junior = correct solution; Mid = clean + follow-ups; Senior = production examples + tradeoffs; Staff = platform/org impact; Principal = multi-year strategy and risk.