Backend Development
Our backend services are built on Node.js, Python, Java, and .NET, architected for production workloads that handle thousands of concurrent requests with sub-200ms response times. We have designed and deployed backend systems for platforms including Affordable Buses, where the API layer manages real-time fleet availability, booking transactions, and payment processing for 56+ vehicles, and pharmacy platforms like Tony's Family Pharmacy, handling prescription management, inventory tracking, and delivery scheduling. Every backend we build includes structured logging, health check endpoints, rate limiting, and automated database migrations. We use PostgreSQL, MongoDB, and Redis depending on data access patterns, and deploy on AWS, Azure, or GCP with infrastructure-as-code using Terraform or CloudFormation. Typical backend projects take 8-14 weeks from architecture review to production, with load testing confirming throughput targets before launch.
Architect RESTful and GraphQL APIs using Node.js (Express/Fastify), Python (Django/FastAPI), Java (Spring Boot), and .NET, selected based on project performance and team requirements.
Design normalized relational schemas in PostgreSQL and MySQL, with read replicas and connection pooling for high-throughput applications.
Implement authentication and authorization using OAuth 2.0, JWT tokens, and role-based access control (RBAC) with refresh token rotation.
Build transactional workflows for payment processing, booking management, and order fulfillment with idempotency guarantees and retry logic.
Deploy caching layers with Redis and Memcached to reduce database load, achieving 10-50x read performance improvements on frequently accessed data.
Set up automated database migrations with version control, zero-downtime schema changes, and rollback procedures for production safety.
Integrate third-party services including payment gateways (Stripe, Square), email providers (SendGrid, SES), and SMS APIs (Twilio) with circuit breaker patterns.
Implement structured logging with correlation IDs, health check endpoints, and application performance monitoring using Datadog or New Relic.
Conduct load testing with k6 or Artillery to validate throughput targets — typically 1,000+ requests per second — before production deployment.
Write unit and integration tests with Jest, pytest, or JUnit, maintaining 80%+ coverage on business logic and API endpoints.