Architecture & Distributed Systems

We review how a system is structured, how it handles data and failures, and how it is built and operated. We read the code, not only the diagrams, and we talk to the people who run it. The result is a short list of risks ranked by business impact, the decisions we recommend, and the trade-offs behind them, recorded as architecture decision records your team can keep.

An order confirmed while the stock was never reserved. A payment debited twice because a retry arrived after a timeout. A status that differs between the CRM and the core system. These are consistency problems, and they have well-understood solutions: the transactional outbox, idempotency keys, sagas with compensating actions, and reconciliation jobs where strict consistency is impossible. We choose the simplest mechanism that makes the business guarantee hold.

Kafka is powerful and unforgiving. We design topics, partitioning and keys for the ordering you actually need, consumer groups that scale, retry and dead-letter strategies that do not lose data, schema evolution that does not break consumers, and replay procedures you can run with confidence during an incident.

Timeouts, retries with backoff, circuit breakers, bulkheads and load shedding: each protects against a specific failure and each has a cost. We apply them where the failure modes justify them, test them, and make sure a slow dependency degrades one feature instead of the whole platform. High availability is designed around the outages you can realistically expect, not around a diagram.

APIs between teams, partners and vendors are long-term commitments. We design contracts with clear error semantics, versioning and backward compatibility, and we choose deliberately between synchronous calls and asynchronous messaging. We have built integrations with banking platforms, trading systems, ERPs and industrial systems, where data formats are strict and mistakes are visible.

Capacity planning based on measurements, load tests that reproduce real traffic patterns, and observability that lets you follow one business transaction across services. Service-level objectives are useful when they guide decisions; we help define the few that matter and the alerts that go with them.