NestJS Developer Interview Questions (2025)

Follow resource-oriented design: nouns in URLs, HTTP verbs for actions, consistent naming conventions, and versioning from day one (/v1/). Use OpenAPI/Swagger for documentation and contract testing. Return appropriate status codes, envelope errors consistently, and paginate all collection endpoints. Authentication and rate limiting belong at the gateway, not scattered across handlers.

Start with EXPLAIN to understand the execution plan. Add indexes for high-cardinality filter and sort columns. Move expensive aggregations to read replicas or materialised views. Cache frequently read, slowly changing data in Redis with a cache-aside pattern. For analytics, consider a separate OLAP store rather than running heavy aggregations against the OLTP database.

Horizontal scaling requires stateless application servers — session state lives in Redis or a token (JWT), not in memory. Design idempotent endpoints so retries are safe. Use a load balancer for traffic distribution. For CPU-bound work, offload to a worker pool. For data-tier scaling, read replicas handle read traffic; sharding or a distributed database handles write volume.

Parameterise all database queries to prevent SQL injection. Validate and sanitise all input at the boundary. Use HTTPS everywhere and HSTS headers. Store passwords with bcrypt or Argon2, never MD5/SHA1. Apply the principle of least privilege to service accounts and API keys. Rotate secrets regularly and store them in a vault, not environment files committed to source control.

Cache-aside (read-through) is the most flexible: check the cache first, fall through to the database on a miss, write to cache on the way back. Set TTLs appropriate to data freshness requirements. Invalidate proactively on write when stale data is harmful, or use event-driven invalidation via pub/sub. Cache at the right layer — HTTP cache headers for public responses, application cache for authenticated data.

Start with a monolith. Microservices make sense when independent teams need to deploy different parts independently, when services have genuinely different scaling requirements, or when a bounded context is mature enough to have stable interfaces. Microservices add network latency, distributed tracing complexity, and deployment overhead — don't pay that cost before the benefits are real.

Authentication verifies identity (who you are); authorisation decides access (what you can do). For user auth, use OAuth 2.0 / OIDC with JWTs for stateless token verification, or session cookies with server-side storage. For service-to-service auth, mutual TLS or signed tokens. Authorisation logic should be centralised (middleware or policy engine like OPA), not scattered across handlers.

Queues decouple producers from consumers and absorb traffic spikes. Introduce them when: a task can be done asynchronously (email, report generation), you need guaranteed at-least-once delivery, or you want to fan out an event to multiple downstream consumers. Choose Kafka for high-throughput ordered streams, RabbitMQ/SQS for task queues. Always handle poison messages with a dead-letter queue.

Log in JSON so logs are machine-parseable. Include a correlation/request ID on every log line for tracing a request across services. Log at appropriate levels: DEBUG for development, INFO for normal operations, WARN for handled degradation, ERROR for unexpected failures. Pair logs with metrics (Prometheus) and distributed traces (OpenTelemetry) so you can move between the three pillars of observability.

Blue-green deployment keeps the old version live while the new one is spun up, then switches traffic atomically — instant rollback is a traffic flip. Canary deployments route a small percentage of traffic to the new version first, expanding as confidence grows. Rolling deployments replace instances incrementally. All strategies require health checks, readiness probes, and graceful shutdown handling so in-flight requests complete before an instance is terminated.

Most placements start within 48 hours. After you submit your requirement, we send 2–3 pre-screened NestJS developer profiles within 24 hours. Once you select a candidate and sign the NDA, we handle onboarding and the developer can begin the same day.

Every NestJS developer goes through a technical screening interview, a live coding exercise specific to NestJS challenges, and a structured communication assessment. We also review their portfolio of shipped work and verify references where available. Only the top 8% of applicants pass.

We offer a 90-day replacement guarantee. If the match isn't working for any reason, your dedicated account manager will find a replacement at no extra cost and manage the transition to minimise disruption to your project.

Absolutely. We encourage it. After we send profiles, you conduct your own technical interview with each candidate. There is no commitment until you choose someone and sign the agreement. We can also arrange a paid 3-day trial task if you want to see the developer work on a real slice of your project.

We offer four models: Dedicated developer (full-time, monthly — ideal for ongoing product work), Fixed-price project (scoped deliverable with a defined budget), Hourly (minimum 10 hours — great for audits or advisory), and Hire a Team (multiple developers under one managed engagement). Your account manager will recommend the right fit based on your timeline and budget.