Distributed Systems & Reliability
I look for the point where a successful request can still leave the business in the wrong state.
What does this work deliver?
Failure-aware workflows with clear recovery and fewer silent inconsistencies.
See the implementation in Notification recovery at Fitter Health.
What problems does this solve?
External providers, databases and message queues rarely fail together or in a predictable order. A payment can succeed before an acknowledgment arrives; an alert can be lost between storage and dispatch. Reliability means designing for those gaps, not assuming a happy-path request.
Good fit for
- Teams integrating payment or notification providers
- Engineers replacing fragile cron or synchronous workflows
- Products where lost events create customer-facing errors
1. Map state and failure boundaries
I distinguish the transaction that creates the business fact from the asynchronous action that follows it. Idempotency records and stable event identifiers help retries converge on one outcome. Duplicate delivery is expected rather than treated as an exceptional bug.
Engineering checks
- Write state-machine transitions
- Define operation identities and deduplication
- Test database commit versus dispatch failure
2. Make external work recoverable
A durable outbox, queue or background job can bridge application transactions and provider calls. Retry schedules must distinguish transient failures from invalid input. Dead-letter records and reconciliation provide a path for investigation without silent data loss.
Engineering checks
- Bound retries and add backoff
- Record permanent failures and operator actions
- Verify webhook authenticity and event ordering
3. Prove the system can be operated
I specify meaningful alerts, repeatable fixtures and recovery runbooks. Strong guarantees are narrow: an idempotent handler improves duplicate safety but cannot magically make every multi-system update atomic.
Engineering checks
- Exercise provider timeout scenarios
- Measure task age and unprocessed events
- Document compensation and repair paths
Expected result and engineering trade-off
Failure-aware workflows with clear recovery and fewer silent inconsistencies.
Retries without idempotency or reconciliation can amplify failures rather than repair them.
Have a backend, data, or product challenge?
Review all service areas, see published work, or contact me about a role or project.
Let’s talk
I like working through complex problems with people who care about the details. Have a product to build, an engineering role, or an interesting challenge? Let’s start a conversation.
SaaimOpen to full-time roles, contract work, and conversations about things worth building.