Dedicated Backend Development Team | Node.js, Python & Go

RESTful and GraphQL API development with the design decisions made deliberately: resource-oriented URL structures, correct HTTP verb semantics, consistent error response format (RFC 9457 Problem Details), and versioning strategy (URI versioning /v1/ vs Accept header negotiation) agreed before the first client integrates. OpenAPI 3.0 specification written contract-first so the spec is the source of truth for both the implementation and the documentation -- not a document manually maintained and left to drift from the code. Code generation from the spec using tools like openapi-generator or orval so the TypeScript client SDK and backend route handlers are always in sync. Authentication built in from the start: OAuth 2.0 with PKCE for user-delegated access, JWT (RS256 asymmetric signing, 15-minute expiry with refresh token rotation) for stateless token validation, API key authentication with per-key rate limits and rotation support. Rate limiting via the token bucket algorithm with IETF RateLimit headers (RateLimit-Limit, RateLimit-Remaining, RateLimit-Reset) so clients know their quota before hitting a 429 rather than after. Pagination using cursor-based patterns (base64-encoded row IDs as stable cursors that don't shift when rows are inserted) rather than offset pagination that produces unreliable results under concurrent writes. Fastify with Zod or JSON Schema validation at the route level rejecting malformed requests before they reach business logic. The API surface your third-party integrators and mobile teams won't write workarounds against.

API security hardening: OWASP API Security Top 10 addressed at implementation time (not retrofitted during a security audit). Object-level authorisation enforced on every endpoint -- every request for a resource checks that the authenticated user owns or has permission to access that specific record, not just that they have the right role. Excessive data exposure prevented by explicit field selection in every query rather than returning the full ORM model and hoping the serialiser filters sensitive fields. Mass assignment protection with strict input whitelists. Helmet.js (Node.js) or equivalent framework security middleware configured for Content-Security-Policy, X-Content-Type-Options, X-Frame-Options, Strict-Transport-Security, and Referrer-Policy headers. API gateway rate limiting at infrastructure level (AWS API Gateway throttling, Kong rate-limit plugin) as a first line of defence, with application-level rate limiting as a second layer for granular per-user or per-endpoint limits.

PostgreSQL schema design based on the query patterns the application actually needs -- not normalised for normalisation's sake when it creates joins that hurt read performance at scale. Indexes designed for the specific WHERE clauses, ORDER BY fields, and JOIN conditions in the actual query workload: partial indexes for filtered queries on boolean or enum columns, composite indexes with correct column ordering (equality conditions first, range conditions last), covering indexes with INCLUDE to eliminate heap fetches for high-frequency read queries. EXPLAIN (ANALYZE, BUFFERS) on slow queries to distinguish index-scan plans from sequential-scan plans, identify bitmap heap scan patterns that benefit from a covering index, and isolate the access pattern change that eliminates the bottleneck without guessing. ORM configuration that doesn't silently generate N+1 queries: Prisma with explicit include chains and select to avoid over-fetching, TypeORM with QueryBuilder for complex joins, Knex for raw control when ORMs produce plans you can't tune. Read replica routing for reporting queries that don't need the primary: pg-pool or Prisma datasource configuration separating write traffic from analytics workload. Connection pooling via PgBouncer in transaction mode for serverless environments where connection count spikes without pg_bouncer overhead. Table partitioning by time range (PARTITION BY RANGE (created_at)) for time-series data tables that would otherwise require expensive sequential scans as row counts grow past 100M. Schema migration management with reversible migrations (Prisma Migrate, Flyway, or Flyway-compatible Liquibase) so rollbacks are possible without manual intervention and migration history is version-controlled.

Payment integrations with Stripe (charge capture, subscriptions with proration, payment method management, webhook handling with idempotency keys that prevent double-processing on retry), PayPal, and Braintree. Communication integrations with Twilio (SMS, voice calls, verification), SendGrid/Postmark for transactional email with delivery event tracking (opens, clicks, bounces). CRM integrations with Salesforce (REST API, SOQL queries, bulk API for large data sync, webhook-to-platform event publishing) and HubSpot. Accounting integrations with Xero, QuickBooks, and NetSuite. All integrations built with: idempotency keys for payment events so retries on failed webhooks don't create duplicate charges; retry logic with exponential backoff and Retry-After header respect to avoid hammering a degraded upstream service; dead letter queues for events that exceed the retry budget -- failed events stored for inspection rather than silently dropped; structured logging that records the external API request, response status, latency, and any transformation applied, so a failed integration event is debuggable from the log without re-triggering it in a staging environment. Webhook endpoint security with HMAC signature verification (Stripe signature, GitHub webhook secret, Twilio X-Twilio-Signature) so only the upstream service can trigger processing and replay attacks are blocked. Monitoring that distinguishes upstream degradation (elevated error rate on external API calls) from application errors in the integration logic, so on-call knows whether the problem requires a code fix or waiting for the upstream provider to recover.

Redis-backed caching for data that changes infrequently but is requested constantly: user profile data, permission structures, product catalogue, configuration values -- with explicit TTLs and cache invalidation on write (MULTI/EXEC transaction to update both the database record and the cache atomically) rather than time-based expiry that serves stale data after an update. Cache-aside pattern with stale-while-revalidate for high-traffic endpoints: serve the cached value immediately while triggering an async background refresh, avoiding the thundering herd problem where a cache expiry for a popular key causes a spike of simultaneous database reads. BullMQ (Redis-backed) for background job queues: email sending, PDF generation, image processing, third-party webhook delivery, and any operation that doesn't need to complete synchronously within the HTTP request lifecycle. Queue configuration with worker concurrency limits (preventing a spike of queued jobs from overwhelming downstream services), retry policies with exponential backoff, and job failure alerting integrated with PagerDuty or Slack so failed background work is visible to on-call. Horizontal scaling preparation: stateless application servers (session tokens validated against Redis or JWT, no in-memory state), database connection pooling via PgBouncer sized to the PostgreSQL max_connections limit divided by the number of application server instances. pg_stat_statements extension enabled to identify the queries accounting for the highest cumulative execution time -- the optimisation targets worth addressing before vertical scaling or adding read replicas. Target: p50 API response under 100ms, p95 under 400ms for typical CRUD endpoints under production load.

Background job observability: BullMQ Dashboard or Taskforce.sh for real-time visibility into queue depth, job processing rate, failed job count, and job latency by queue. Failed jobs logged with the full job data and stack trace, queryable without connecting directly to Redis. Bull Board integration within the admin panel for operations teams who need to inspect or retry failed jobs without engineer involvement. Worker memory leak detection: Node.js --inspect flag with periodic heap snapshot comparison in staging to catch memory growth patterns before they cause worker OOMKills in production.

Event-driven architecture patterns implemented where appropriate: domain events published to a message bus (AWS EventBridge, Redis pub/sub, or RabbitMQ) to decouple cross-domain actions (user registered → welcome email, order placed → inventory decrement, payment succeeded → invoice generated) without tight coupling between services. Saga pattern for multi-step distributed transactions that require compensating actions if a step fails (order creation saga: reserve inventory → charge payment → create fulfilment record; if payment fails, inventory reservation is released via a compensating action). Outbox pattern for reliable event delivery: the domain event is written to an outbox table in the same database transaction as the business record, then a background process reads the outbox and publishes to the message bus -- guaranteeing events are never lost even if the message bus is temporarily unavailable at the time of the write.