Backend & API Engineering

We build backends — typically async Python services on FastAPI — engineered around the request shapes they'll actually serve: connection pooling sized to the database's real limits, Redis caching for the reads that dominate traffic, background queues so slow work never blocks a user-facing request, and WebSocket or server-sent-event channels where clients need live updates. Long-running AI calls get their own path: queued, streamed, and isolated so they can't starve the fast endpoints.

Performance is treated as a measured property, not an adjective. Endpoints ship with load-tested p95 and p99 latency figures, profiling identifies the queries and serialization hot spots worth fixing, and the ones that aren't worth fixing are documented as known trade-offs. When something later gets slow, the instrumentation already in place says exactly where.

Database architecture starts from the queries, not the entities: we design PostgreSQL schemas around the access patterns the product actually has, index for the reads that dominate, and keep write paths clean so contention doesn't appear at the worst possible traffic level. Migrations are versioned and reversible, run as part of deployment, and rehearsed against production-shaped data so a schema change is a step in the pipeline rather than a weekend event.

Data pipelines move information reliably between systems: event-driven ingestion for data that must flow continuously, scheduled batch jobs for the rest, idempotent processing so replays and retries never duplicate records, and validation at the boundaries so bad data is quarantined instead of silently corrupting downstream tables. Backups are automated and — the part most teams skip — restores are actually tested.