Backend Developer Interview Prep Guide
Prepare for backend developer interviews with API rate limiter design, distributed systems deep-dives, database optimization strategies, and real system design questions asked at Amazon, Stripe, and Google.
Last Updated: 2026-02-11 | Reading Time: 10-12 minutes
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Interview Types
Key Skills to Demonstrate
Top Backend Developer Interview Questions
Design an API rate limiter that works across a distributed fleet of servers, supporting per-user and per-endpoint limits.
This is a top question at Stripe and Cloudflare. Discuss token bucket vs sliding window log vs sliding window counter algorithms. For distributed rate limiting, explain using Redis with atomic INCR and EXPIRE operations. Cover: how to handle Redis failures (fail open vs fail closed), the tradeoff between accuracy and performance, returning proper HTTP 429 responses with Retry-After headers, and different tiers for free vs paid users. Discuss how Stripe actually uses a combination of token bucket per user and global rate limiting.
Design a payment processing system that handles millions of transactions per day with exactly-once semantics and full auditability.
This is a real Amazon and Stripe interview question. Cover: idempotency keys for exactly-once processing, saga pattern or two-phase commit for distributed transactions, event sourcing for complete audit trail, dead letter queues for failed transactions, PCI DSS compliance considerations, and webhook delivery to merchants with retry logic. Discuss the tradeoff between synchronous (user waits) vs asynchronous (eventual confirmation) processing.
Explain eventual consistency versus strong consistency. When would you choose each, and what are the practical implications for your application?
Go beyond textbook definitions. Give real examples: DynamoDB defaults to eventually consistent reads (cheaper, faster) but offers strongly consistent reads at 2x cost. PostgreSQL with synchronous replication gives strong consistency but higher write latency. Discuss specific scenarios: a shopping cart can tolerate eventual consistency (brief stale reads), but an inventory counter for the last item in stock needs strong consistency to prevent overselling. Mention Cassandra tunable consistency levels and how QUORUM reads/writes provide a middle ground.
How would you design the backend for a collaborative editing service like Google Docs?
This is a harder system design question asked at Google. Cover the two main approaches: Operational Transformation (OT, used by Google Docs) and Conflict-free Replicated Data Types (CRDTs, used by Figma). Discuss: WebSocket connections for real-time sync, operation ordering and conflict resolution, cursor presence tracking, version history with snapshots, and scaling the collaboration service horizontally. You do not need to implement OT or CRDTs, but explain the tradeoffs: OT requires a central server, CRDTs work in peer-to-peer but have higher memory overhead.
Your API endpoint is returning p99 latency of 2 seconds when the SLO is 500ms. Walk me through how you diagnose and fix this.
Show systematic debugging: start with distributed tracing (Jaeger/Datadog) to identify which service or database call is slow. Check: N+1 queries (common culprit), missing database indexes, connection pool exhaustion, downstream service timeouts, garbage collection pauses, and lock contention. Solutions: add indexes based on EXPLAIN ANALYZE, implement read-through caching with Redis, add connection pooling (PgBouncer), optimize queries with JOINs instead of multiple round trips, and add circuit breakers for slow dependencies.
Design a REST API for a social media application. Compare it with a GraphQL alternative and explain when you would choose each.
Design RESTful resources: /users, /posts, /comments with proper HTTP verbs, pagination (cursor-based), filtering, and error responses. Then discuss GraphQL tradeoffs: GraphQL solves over-fetching (mobile clients get exactly what they need) but introduces complexity in caching (no HTTP caching), authorization (field-level permissions), and N+1 queries (use DataLoader). REST is better for simple CRUD APIs with well-defined clients; GraphQL shines for complex data graphs with diverse clients. Mention that many companies (Shopify, GitHub) use both.
How do you handle database migrations in production with zero downtime?
Discuss the expand-and-contract pattern: first add the new column (nullable or with default), deploy code that writes to both old and new columns, backfill existing data, deploy code that reads from the new column, then drop the old column. Cover: migration testing in staging with production-like data, rollback strategies, using tools like Flyway or Alembic for version control, locking considerations (avoid full table locks on large tables), and online DDL tools like pt-online-schema-change for MySQL or pg_repack for PostgreSQL.
Tell me about a time you identified and resolved a significant performance bottleneck in a production system.
Be specific and data-driven. For example: "I noticed our order processing pipeline was taking 12 seconds for large orders. Using Datadog APM, I traced it to sequential API calls to our inventory service, one per line item. I implemented batch API endpoints and parallel processing, reducing the time to 800ms. The change improved our checkout completion rate by 8% and reduced timeout errors by 95%." Show the investigation method, not just the fix.
How to Prepare for Backend Developer Interviews
Study Distributed Systems Through Real-World Case Studies
Read engineering blogs from companies that operate at scale: Stripe (idempotency and payment processing), Netflix (chaos engineering and resilience), Uber (geospatial systems and real-time dispatch), and Amazon (DynamoDB paper, cell-based architecture). For each system you study, understand: what problem they were solving, what tradeoffs they made, what failed and how they recovered. This gives you realistic examples for system design interviews rather than textbook answers.
Master Database Internals, Not Just SQL Syntax
Backend interviews increasingly go deep on database knowledge. Understand: B-tree vs LSM-tree index structures (PostgreSQL vs RocksDB), MVCC and transaction isolation levels (read committed vs serializable and the anomalies each prevents), query optimizer behavior (why your query might not use the index you expect), connection pooling (why 100 connections per server is often too many), and replication topologies (synchronous vs asynchronous, leader-follower vs multi-leader). Practice reading and explaining EXPLAIN ANALYZE output for PostgreSQL or execution plans for your preferred database.
Practice API Design as a Standalone Skill
Design RESTful APIs with proper resource modeling, consistent error responses (RFC 7807 problem details), pagination (cursor-based for feeds, offset-based for finite collections), versioning strategy (URL path vs header), rate limiting headers, and idempotency keys for mutation endpoints. Also understand gRPC (when to use it: internal microservice communication with strict schemas) and GraphQL (when to use it: diverse clients with varied data needs). Practice designing the API for a ride-sharing app, a payment gateway, and a content management system.
Learn Observability and Incident Response Patterns
Modern backend developers must understand production operations. Know the four golden signals (latency, traffic, errors, saturation), how to set up structured logging (JSON logs with correlation IDs), distributed tracing (OpenTelemetry), metrics and alerting (Prometheus/Grafana, Datadog), and on-call best practices. Prepare a story about a production incident you were involved in: what broke, how you detected it, how you triaged, what the root cause was, and what preventive measures you implemented. This is tested in behavioral rounds at every company.
Backend Developer Interview Formats
System Design Round
Design a backend system for a real-world scenario in 45-60 minutes. Common prompts: design a URL shortener, a notification system, a rate limiter, or a payment processing pipeline. You are expected to gather requirements, draw a high-level architecture, design the API and database schema, discuss scaling and reliability, and analyze tradeoffs. At Amazon, this is called the "System Design" or "Bar Raiser" round. At Google, expect deep follow-up questions about specific components.
Coding Round
Solve 1-2 algorithmic problems with emphasis on clean, production-quality code. Backend-specific problems often involve: graph traversal (dependency resolution), concurrent data structures, rate limiting implementations, or data processing pipelines. You are evaluated on correctness, efficiency, code organization, error handling, and testing approach. At Stripe, coding rounds use realistic scenarios like parsing financial data or implementing retry logic.
Architecture Review / Deep Dive
Present and defend a system you have built or discuss the architecture of an existing system and propose improvements. At Amazon, this often takes the form of "Tell me about the most complex system you have built" followed by 30 minutes of deep questions. At Datadog and Cloudflare, you might review a design document and identify issues. You are evaluated on depth of understanding, ability to discuss tradeoffs, and experience with production systems.
Common Mistakes to Avoid
Over-engineering the solution with microservices and Kubernetes for every problem
Start with the simplest architecture that meets the requirements. If the system serves 1,000 users, a monolith with PostgreSQL is the right answer. Add microservices only when you can articulate the specific problem they solve: independent deployment of a team, different scaling requirements, or different technology needs. In interviews, explicitly state: "For this scale, I would start with X, and here is when I would consider splitting into Y."
Not discussing security considerations unless explicitly asked
Proactively mention security in every system design answer: authentication (OAuth 2.0, JWT with refresh token rotation), authorization (RBAC or ABAC), input validation and parameterized queries (prevent SQL injection), encryption at rest and in transit (TLS 1.3), rate limiting, and OWASP Top 10 relevant protections. Mentioning security without being prompted signals senior-level thinking and is noticed by interviewers.
Designing systems without mentioning monitoring, logging, or alerting
Every production system needs observability. In your system design answers, always include: structured logging with request correlation IDs, metrics dashboards for the four golden signals, alerting with meaningful thresholds (not just "CPU > 80%"), distributed tracing for microservice architectures, and health check endpoints. This is what separates candidates who have built real systems from those who have only studied textbooks.
Weak understanding of database performance at scale
Backend developers are expected to go deep on databases. Practice: writing queries and analyzing execution plans, designing schemas with proper indexing strategies, understanding connection pool sizing (Little Law for optimal pool size), planning for database scaling (read replicas first, then vertical scaling, then sharding as a last resort), and explaining the tradeoffs of different consistency models. If you cannot explain why a query is slow by reading its execution plan, invest time in this skill before interviewing.
Backend Developer Interview FAQs
What programming language should I use for backend interviews?
Choose based on the role requirements or your strongest language. Python (most versatile, great for interviews), Java (strong for system design discussions, shows type safety knowledge), Go (increasingly popular for infrastructure and high-performance services), and Node.js (common for full-stack-leaning roles) are all excellent choices. Companies like Stripe and Datadog value Go experience. Amazon is language-agnostic. Google prefers C++, Java, Python, or Go. The key is fluency: you should be able to write idiomatic code without looking up syntax.
How deep should I know databases for backend interviews?
Deeper than most candidates prepare. You should know: ACID properties and what violating each one means practically, indexing strategies (when to use composite indexes, covering indexes, partial indexes), query optimization (reading EXPLAIN output, understanding sequential vs index scan choices), replication and consistency models (synchronous vs asynchronous replication, eventual consistency tradeoffs), and when to choose SQL vs NoSQL (PostgreSQL vs DynamoDB vs MongoDB). Practice designing schemas for real applications and explaining your indexing strategy.
How important is cloud knowledge for backend developer interviews?
Important but not as deep as for DevOps or Cloud Architect roles. You should know: compute options (EC2/Lambda/ECS and when to use each), storage services (S3, RDS, DynamoDB), messaging (SQS, SNS, Kafka), and load balancing (ALB vs NLB). You do not need to know every service, but you should be able to design a system using cloud primitives and discuss the tradeoffs between managed services and self-hosted solutions.
Do backend developers need to know frontend technologies?
Enough to design good APIs. Understand how frontend applications consume your APIs: CORS configuration, authentication token flow, pagination patterns that work well for UIs, and what data shape is optimal for the client. You do not need deep React or CSS knowledge, but understanding how your API design decisions affect the frontend developer experience makes you a more effective backend engineer and better system designer in interviews.
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Last updated: 2026-02-11 | Written by JobJourney Career Experts