Backend & Platform Engineering

API design and development

We design APIs as contracts: versioned from the first release, documented from the schema, and consistent enough that your mobile team stops guessing. REST where it serves you, GraphQL where clients genuinely need shaped queries, gRPC where service-to-service latency matters. Authentication and authorization are engineered in — OAuth flows, session management, role and permission models — never bolted on.

Every API ships with the unglamorous parts that decide production behaviour: rate limiting, idempotency keys on mutating endpoints, pagination that survives concurrent writes, and error responses a client can actually branch on.

Database architecture

PostgreSQL is our default and we defend that choice: transactional integrity, mature tooling, and one system that handles relational data, JSON documents, full-text search, and vector search (pgvector) before you need specialised stores. Schema design, indexing strategy, and query plans are engineering decisions we make explicitly and document — the boring foundations that decide whether year-two features take days or rewrites.

Where the workload genuinely calls for more, we add it deliberately: Redis for caching and queues, MongoDB where document flexibility wins, ClickHouse for analytics volumes Postgres shouldn't carry. Migrations are reviewed SQL, run with rollback paths, never ORM magic applied blind.

Real-time and event-driven systems

Chat, live dashboards, order tracking, collaborative editing — real-time features fail differently: reconnection storms, message ordering, consumers that lag silently. We build them on WebSockets and event streams (Kafka, or Postgres-backed queues at smaller scale) with delivery semantics chosen deliberately and dead-letter handling from the start, because 'at-least-once' versus 'exactly-once' is a product decision, not a footnote.

Cloud infrastructure and operations

We run workloads on AWS, Azure, and Google Cloud with infrastructure defined in Terraform — reproducible environments, reviewable changes, no snowflake servers. Containers and Kubernetes where scale justifies them; simpler single-node deployments where they don't, stated plainly. Every system ships with observability wired in: structured logs, metrics, tracing via OpenTelemetry, and alerts tuned to page on symptoms, not noise.

Security is reviewed at every layer — secrets management, least-privilege IAM, dependency auditing, TLS everywhere — because the cheapest security incident is the one made structurally unlikely.

Scaling and modernizing existing backends

We take over systems under strain: the monolith at its vertical-scaling ceiling, the API whose p99 walked past two seconds, the codebase whose original team left. Work starts with measurement — profiling, query analysis, load characteristics — because guessed optimisations waste the budget real bottlenecks need. Then we fix in risk order: quick wins first, structural changes behind feature flags, a strangler migration only when the numbers justify it.