Backend engineering interviews test depth across a wide surface area — algorithms, databases, distributed systems, and API design. This guide covers what to prepare for senior backend roles at top tech companies in 2026.
The Backend Interview Loop
- Technical screen — 1–2 coding problems (45 min)
- Onsite / virtual loop (4–5 rounds):
- Algorithms + data structures (coding)
- System design
- Database design / SQL
- Distributed systems / architecture
- Behavioral
Coding: Algorithms That Backend Engineers Need
Backend coding interviews focus on practical algorithmic patterns:
- Graphs: BFS/DFS, shortest path (Dijkstra), topological sort (dependency resolution)
- Trees: BST operations, LCA, serialization/deserialization
- Hashing: Consistent hashing (for distributed systems context)
- Two pointers + sliding window: Streaming data processing
- Priority queues / heaps: Top-K, median of data stream, task scheduling
- String processing: Parsing, tokenization, pattern matching
Databases: SQL + NoSQL + Design
Database knowledge is tested more thoroughly in backend roles than SWE generalist roles.
SQL mastery required:
- Window functions:
ROW_NUMBER,RANK,LAG/LEAD,NTILE - CTEs and recursive CTEs (hierarchical data, org charts)
- Index design: B-tree vs hash indexes, composite index ordering, covering indexes
- Query optimization:
EXPLAIN ANALYZE, avoiding N+1, join order - Transactions: ACID properties, isolation levels, deadlock avoidance
NoSQL trade-offs:
- Document stores (MongoDB): denormalization, embedded vs referenced documents
- Key-value stores (Redis, DynamoDB): access patterns drive design
- Time-series databases: when to use InfluxDB or TimescaleDB
- Search engines (Elasticsearch): inverted index, relevance scoring
Database design interview pattern: "Design a database schema for a ride-sharing app."
- Strong answer addresses: normalization vs denormalization trade-off, indexing strategy for query patterns, sharding key for horizontal scale, handling soft deletes.
Distributed Systems
This is where backend interviews get deep. Core topics:
Fundamentals:
- CAP theorem: Can only guarantee 2 of Consistency, Availability, Partition tolerance
- PACELC: Extension of CAP — also considers latency trade-offs in normal operation
- Consistency models: strong, eventual, causal, read-your-writes
- Consensus: Paxos, Raft — you need the concept, not full derivation
Patterns you must know:
- Consistent hashing: For distributed caches, sharding without full reshuffling
- Leader election: How Raft works, split-brain prevention
- Saga pattern: Distributed transactions without 2PC
- Circuit breaker: Failure isolation in service meshes
- Idempotency: Why it matters, how to implement with idempotency keys
Common distributed systems questions:
- "How do you handle duplicate message delivery in a queue consumer?"
- "What's the difference between at-least-once and exactly-once delivery?"
- "How would you implement a distributed lock?"
- "Your service calls 5 downstream services — one is slow. How do you prevent cascade failure?"
API Design
- RESTful design: resource naming, HTTP verb semantics, status codes
- Pagination: cursor vs offset — when each is appropriate
- Versioning: URL vs header versioning, breaking vs non-breaking changes
- Rate limiting: token bucket vs leaky bucket vs fixed window
- Idempotency: PUT vs PATCH, idempotency keys for POST
- GraphQL trade-offs: overfetching vs N+1, when REST is still better
- gRPC: when to use over REST (internal services, streaming, strict contracts)
7-Week Backend Interview Prep Plan
| Week | Focus | |------|-------| | 1 | LeetCode: graphs, trees, heaps (35 problems) | | 2 | SQL deep dive: window functions, query optimization | | 3 | Database design: 5 schema designs with indexing strategy | | 4 | Distributed systems: CAP, consensus, patterns | | 5 | System design: 4 backend-heavy designs | | 6 | API design + behavioral stories | | 7 | 3 full mock loops |
Practice your backend interview walkthroughs — especially distributed systems reasoning — with CareerLift.ai. Explaining CAP theorem trade-offs out loud is a different skill than knowing them on paper.