Tell me about yourself as a backend developer

Start with your current role and the backend responsibilities you own, then highlight 2–3 recent accomplishments that relate to the job. Focus on technologies, scale, and outcomes, for example the services you built, the traffic you supported, or latency you reduced. Here is a short structure: 'I am currently a backend developer at X, where I built Y service that handles Z requests per minute and reduced latency by N percent. Previously, I worked on A and B which taught me about reliability and testing.' Practice this to keep it under two minutes and avoid reciting your full resume. Tailor the examples to the job description and mention one area you want to grow in so you sound motivated.

How would you design a scalable system to handle high traffic

Start by clarifying requirements, like peak requests per second, latency targets, and failure tolerance. Use a shared design approach: break the system into services, add load balancers, scale stateless components horizontally, and pick storage that meets throughput and consistency needs. Give a concrete example: for a public API I would put a CDN in front of static assets, use an API gateway for routing and rate limiting, and deploy stateless application instances behind an autoscaling group with a managed database and read replicas. Include async processing for long-running tasks using queues so user-facing latency stays low. Call out trade-offs explicitly, for example eventual consistency in favor of availability, or increased operational complexity from many microservices. Avoid designing for theoretical extremes, instead propose incremental steps to meet the next set of requirements.

Explain REST versus GraphQL and when you would use each

Describe REST as resource-oriented, with multiple endpoints and standard HTTP verbs, and GraphQL as a query language that exposes a single endpoint where clients request precise fields. Choose REST for simple CRUD services, caching, and public APIs, and choose GraphQL when clients need flexible queries or to reduce overfetching. Give a practical example: use REST for a billing service where endpoints map to invoices and payments, because caching and idempotency are straightforward. Use GraphQL for a product catalog that powers web and mobile apps so clients can request only the fields they need and reduce round trips. Point out gotchas: GraphQL can make caching complex and may require query depth limits to avoid expensive queries. Avoid adding GraphQL unless clients truly benefit from the flexible query model.

How do you ensure data consistency in a distributed system

Start by identifying the consistency model the business requires, whether strong consistency, causal consistency, or eventual consistency. Use techniques like transactions when strong consistency is needed, or design compensating actions and idempotent operations when eventual consistency is acceptable. For example, if you need to update inventory and create an order in different services, consider using the saga pattern with compensating transactions or a two-phase commit if the database supports distributed transactions and the performance cost is acceptable. Prefer idempotent endpoints and unique request IDs to make retries safe. Watch for common pitfalls such as race conditions during concurrent updates and hidden retry storms caused by timeouts. Document the consistency guarantees for each API so clients know what to expect.

What is ACID and how does it compare to BASE

Define ACID as Atomicity, Consistency, Isolation, Durability which applies to traditional relational databases and strong transactional guarantees. Define BASE as Basically Available, Soft state, Eventual consistency which describes many distributed NoSQL systems that favor availability and partition tolerance. Give an example: use ACID transactions for financial transfers where you cannot tolerate lost or partial updates, and use a BASE system for a large product catalog where eventual consistency is acceptable and higher availability is needed. Explain that some systems combine approaches, for example using transactional boundaries inside a service and eventual consistency across services. Highlight trade-offs: ACID systems can limit scalability and raise contention, while BASE systems require careful design for conflict resolution and observability. Make the choice based on correctness needs, latency targets, and expected scale.

How do database indexes work and when can they hurt performance

Explain that indexes are data structures, often B-trees or hash maps, that let the database find rows without scanning the whole table, improving read performance for filtered queries. Point out that indexes add overhead on writes, increase storage, and can slow down large bulk operations. Give an example: adding an index on a user_id column speeds up lookups by that key, but adding many indexes to a high-write table can reduce insert and update throughput. Recommend monitoring index usage and removing unused indexes, and consider partial or composite indexes when queries filter on multiple columns. Warn against indexing columns with low cardinality or indexing every searchable column by default. Regularly review query plans to confirm indexes are actually used.

How would you optimize a slow SQL query

Begin by reproducing the problem and capturing the query plan to see whether the database uses indexes or does full scans. Look for low-hanging improvements like adding appropriate indexes, rewriting joins to reduce row multiplicity, and avoiding SELECT * in favor of required columns. As an example, if a join causes a nested loop over many rows, consider adding indexes on join keys or switching to a hash join by rewriting the query, and ensure statistics are up to date so the planner chooses a good plan. Also evaluate whether denormalization or materialized views make sense for read-heavy workloads. Be cautious with premature denormalization that complicates writes, and always benchmark changes on realistic datasets. Use EXPLAIN and production-like data when testing to avoid misleading conclusions from small samples.

How do you approach caching and cache invalidation

Describe common strategies such as cache-aside where the application reads from cache and falls back to the source on miss, and write-through or write-behind depending on consistency needs. Choose TTLs, versioning, or explicit invalidation to manage staleness based on how fresh the data must be. Provide a concrete technique: for user profile data, use cache-aside with a short TTL and publish invalidation events to a message bus when profiles change so other instances clear or refresh the cache. For heavily read data that is updated rarely, consider longer TTLs or prewarming the cache after deployments. Highlight the cache invalidation problem as a common source of bugs and partial failures, and recommend tracking metrics like cache hit rate and stale reads. Avoid relying solely on long TTLs for frequently updated data without a solid invalidation plan.

How would you version APIs to maintain backward compatibility

Start by choosing a versioning approach such as URI versioning, header versioning, or content negotiation, and be consistent across your APIs. Aim to add non-breaking changes like new optional fields without bumping the major version, and use semantic versioning for clear upgrade signals. Give a specific policy example: keep v1 running while you develop v2, deprecate endpoints with a clear schedule and migration guide, and provide feature flags for gradual rollouts. Document breaking changes and provide client libraries or examples to make migrations easier for consumers. Avoid removing fields or changing types without a planned deprecation period, and include tests that validate backward compatibility. Monitor client usage to know when old versions can be retired safely.

How do you respond to and debug production outages

Describe an incident response process that starts with reducing user impact, for example routing traffic away from failing regions, applying rate limits, or temporarily disabling nonessential features. Use logs, distributed traces, and health metrics to narrow the fault domain and identify the root cause while communicating status to stakeholders. Give an example workflow: during an outage you would open an incident channel, assign roles for investigation and customer communication, gather recent deploys and config changes, and use tracing to find the failing service or slow dependency. After recovery, run a blameless postmortem that captures timeline, root cause, action items, and owners. Emphasize practicing runbooks and automating common mitigations to shorten mean time to recovery. Avoid finger pointing and focus the team on learning, and make sure action items have deadlines and follow-up.

Describe a time you fixed a critical production bug under time pressure

Situation and Task: Our payment service started returning 5xx errors during peak traffic, causing failed transactions and customer complaints. I was on call and needed to restore payment functionality quickly while preserving data integrity. Action: I opened a focused incident channel, collected recent deploys and logs, and used distributed tracing to find a new library that caused blocking on a dependency. I rolled back the deploy to the last known good version, added a temporary rate limiter to protect downstream databases, and prepared a patch to remove the problematic call. Result: Recovery took under 20 minutes after rollback, transaction failures dropped to zero, and we completed a root cause postmortem that led to stricter deploy checks and a rollback playbook. The follow-up actions reduced similar incidents by preventing risky runtime changes without feature flags.

Tell me about a time you improved system performance

Situation and Task: Our user search API was slow during peak times, causing page timeouts and poor user experience. I was asked to reduce p95 latency and increase throughput within one quarter. Action: I measured current latency with traces and profiled the service to find hotspots, discovering that repeated N+1 queries and an unindexed filter caused most delays. I introduced query batching, added appropriate indexes, and added a cache for popular searches while writing regression tests to prevent future performance regressions. Result: The p95 latency improved from 600ms to 120ms and throughput increased by 2.5x, leading to better user retention during peak periods. The changes were rolled out gradually and monitored, and I documented the fixes so the team could reproduce the improvements.

Give an example of when you disagreed with a technical decision and how you handled it

Situation and Task: The team proposed splitting a monolith into many microservices quickly, which I felt would add too much operational overhead for the current traffic and team size. My task was to present alternative approaches while keeping the project timeline intact. Action: I gathered data on current request patterns, deployment cadence, and team capacity, and created a proposal for a phased decomposition that started with clear service boundaries and a strangler pattern. I presented the trade-offs, risks, and a migration plan that included metrics and checkpoints, and recommended adding API contracts and observability before splitting. Result: The team adopted the phased plan, which reduced operational surprises and allowed us to validate boundaries before full separation. The staged approach saved several weeks of rework and kept the team focused on high-value decoupling steps.

backend developer Interview Questions: Complete Guide

Expect a mix of technical whiteboard questions, system design discussions, and behavioral interviews when preparing for backend developer interview questions. Interviews often include live coding or take-home tasks, a system design round, and a cultural or team fit conversation, so prepare examples and practice explaining trade-offs clearly.

Common Interview Questions

Behavioral Questions (STAR Method)

STAR Method: Structure your answers using Situation, Task, Action, and Result to tell compelling stories about your experience.

Questions to Ask the Interviewer

Show your interest by asking thoughtful questions

•What does success look like for this role after the first six months, specifically in terms of system ownership and metrics?
•Can you describe the team structure, who this role will collaborate with most, and the current on-call expectations?
•What are the biggest technical challenges the team is facing right now, and what recent attempts have you made to solve them?
•How does the team measure reliability and performance, and which observability tools and dashboards do you rely on?
•What is the deployment and release process, and how often are production changes made for backend services?

Interview Preparation Tips

Practice explaining trade-offs clearly, for example picking availability over consistency and why, so interviewers see your decision process.

Prepare two system design examples you know well and walk through requirements, components, failure modes, and scaling steps during the interview.

When answering coding questions, speak your thought process, write clear pseudocode, and add tests or edge case handling before finishing.

Have three concise stories ready using the STAR format for reliability, team conflict, and performance improvements, and practice delivering them in under three minutes each.

Overview

This guide prepares you for backend developer interviews by breaking down the common question types, target skills, and realistic expectations. Interviewers evaluate three core areas: coding (algorithms and data structures), system design (APIs, databases, scaling), and operational knowledge (testing, monitoring, security).

For example, you might be asked to optimize an API expected to handle 1 million requests per day — you should discuss rate limiting, caching, and horizontal scaling.

Expect 45–60 minute technical screens and 60–90 minute on-site/system-design interviews. In the coding round, demonstrate clear thought: state assumptions, write correct pseudocode, and test edge cases.

In a design interview, quantify your decisions — estimate throughput, latency targets (e. g.

, 200 ms P95), and storage needs (e. g.

, 500 GB/month). For troubleshooting or debugging questions, cite specific tools you would use, such as a profiler to find a 30–50% CPU hotspot or flamegraphs to locate memory leaks.

Behavioral assessments examine ownership, conflict resolution, and how you ship features with CI/CD pipelines.

Actionable takeaways:

•Practice 40–60 coding problems over 6 weeks, focusing on arrays, hashes, and trees.
•Run 3 mock system-design interviews and prepare one detailed case study: design an API for 1M daily users.

Key Subtopics and Sample Questions

Break your preparation into focused subtopics. Below are high-impact areas with concrete examples and what interviewers expect.

•APIs and Protocols
•Sample: "Design a REST endpoint for uploading images with size limits and versioning."
•Expectation: define routes, request/response shapes, status codes, and a short plan for authentication (JWT/OAuth). Discuss multipart upload and S3-backed storage.

•Databases and Storage
•Sample: "When would you choose a SQL vs NoSQL solution–
•Expectation: compare ACID vs eventual consistency, indexing strategies (explain how a compound index can cut query time from seconds to milliseconds), and backup/retention policy.

•System Design and Scaling
•Sample: "Design a URL shortener for 100M links."
•Expectation: propose sharding, load balancers, data partitioning, and estimate storage (e.g., 100M records × 100 bytes ≈ 10 GB) and throughput.

•Performance and Reliability
•Sample: "How would you reduce 95th percentile latency by 50%–
•Expectation: mention caching with hit-rate targets (70–95%), connection pooling, asynchronous workers, and metrics to monitor.

•Security and Testing
•Sample: "Describe how to prevent SQL injection and add integration tests for an API."
•Expectation: parameterized queries, CI pipelines, and end-to-end test coverage targets (e.g., 80% for core flows).

Actionable takeaway: prioritize 4–6 subtopics, allocate study time (e. g.

, 40% coding, 35% design, 25% ops) and practice with measurable goals.

Recommended Resources and Study Plan

Use these targeted resources and a time-boxed plan to prepare efficiently.

•Practice platforms
•LeetCode/HackerRank: solve 25–40 backend-relevant problems (hash maps, trees, concurrency). Aim for 3 problems per week over 8–12 weeks.
•GitHub: study donnemartin/system-design-primer for design patterns and example answers.

•Books and articles
•"Designing Data-Intensive Applications": read 2–3 chapters per week; focus on replication, partitioning, and stream processing.
•Blog posts on real outages (e.g., postmortems) to learn root-cause analysis.

•Tools and hands-on
•API testing: Postman or curl; measure response times and error rates.
•Load testing: k6 or JMeter to simulate 1k–10k RPS; aim to identify bottlenecks and reduce error rate below 0.1%.
•Observability: set up Prometheus + Grafana to track latency percentiles (P50, P95, P99).

•Interview prep and mock interviews
•Schedule 6–8 mock interviews with peers or platforms; get feedback on communication and trade-offs.

8-week study plan (example):

•Weeks 1–4: 60% coding, 40% fundamentals (DBs, HTTP)
•Weeks 5–6: 70% system design, 30% ops
•Weeks 7–8: 50% mocks, 50% targeted weak spots

Actionable takeaway: follow the 8-week plan, measure progress weekly, and iterate on weak areas with focused practice.

backend developer Interview Questions: Complete Guide

Emily Thompson

Common Interview Questions

Behavioral Questions (STAR Method)

Questions to Ask the Interviewer

Interview Preparation Tips

Overview

Key Subtopics and Sample Questions

Recommended Resources and Study Plan

Interview Prep Checklist

Build your job search toolkit

backend developer Interview Questions: Complete Guide

Emily Thompson

Common Interview Questions

Q1Tell me about yourself as a backend developer

Q2How would you design a scalable system to handle high traffic

Q3Explain REST versus GraphQL and when you would use each

Q4How do you ensure data consistency in a distributed system

Q5What is ACID and how does it compare to BASE

Q6How do database indexes work and when can they hurt performance

Q7How would you optimize a slow SQL query

Q8How do you approach caching and cache invalidation

Q9How would you version APIs to maintain backward compatibility

Q10How do you respond to and debug production outages

Behavioral Questions (STAR Method)

B1Describe a time you fixed a critical production bug under time pressure

B2Tell me about a time you improved system performance

B3Give an example of when you disagreed with a technical decision and how you handled it

Questions to Ask the Interviewer

Interview Preparation Tips

Overview

Key Subtopics and Sample Questions

Recommended Resources and Study Plan

Interview Prep Checklist

Build your job search toolkit