Backend Engineering Interview Handbook¶
A practical, continuously maintained handbook for backend, platform, systems, and GenAI engineering interviews.
Backend engineering is the central track. DSA, system design, computer science fundamentals, cloud, DevOps, and production GenAI engineering provide complementary depth.
The goal is not to collect trivia. Each topic should build durable engineering understanding through clear explanations, runnable examples, trade-offs, production scenarios, and interview questions with reasoned answers.
Learning tracks¶
- Backend engineering
- System design
- Python
- Data model, memory, and garbage collection
- Go
- Java
- Java concurrency for backend services
- Data Structures & Algorithms
- Arrays, strings, hash maps, and sets
- GenAI engineering
- Cloud
- DevOps
- Interview preparation
Start here¶
- API authentication and authorization
- Application caching: consistency, invalidation, and stampede protection
- SQL indexes, query plans, transactions, and isolation
- Database connection pooling for backend services
- Distributed leases and fencing tokens
- Designing a URL shortener
- Database sharding for multi-tenant backends
- AWS VPC networking for backend services
- AWS IAM for backend workloads
- Python data model, memory, and garbage collection
- Python concurrency: threads vs. asyncio vs. processes
- Java concurrency for backend services
- DSA learning path
- GenAI engineering learning path
- Contribution guidelines
- Editorial roadmap
Quality standards¶
Every substantial topic should explain the problem being solved, architecture and data flow, implementation choices, failure modes, security implications, operational metrics, and trade-offs. Code should be runnable and tested where practical. Version-specific behavior must be identified. Prefer official documentation and primary sources.
Accuracy and usefulness take priority over commit frequency. No duplicate questions or filler commits.