Data & Integration Platform

A representative integration platform that ingests data from multiple external APIs, processes it asynchronously through a job queue, and lands validated, transformed records in PostgreSQL — built to stay reliable when third-party systems are not.

The team needed to integrate with several external APIs that varied widely in reliability, rate limits, and response times. Processing these synchronously created cascading slowdowns and required significant manual intervention whenever an integration failed partway through.

• Ingest and process data from multiple external APIs with different reliability characteristics.
• Recover automatically from transient third-party failures without manual replay.
• Increase throughput without proportionally increasing manual data-handling effort.
• Provide clear visibility into failures so incidents can be diagnosed quickly.

• Third-party APIs have rate limits and occasional outages that are outside the team's control.
• Data must be validated and normalized before being treated as authoritative, since upstream formats are inconsistent.
• The platform had to interoperate with an existing PostgreSQL schema used by other services.

An integration gateway receives and authenticates inbound data (or polls external APIs), performing initial validation before handing work off to a job queue (BullMQ) rather than processing inline. A pool of workers pulls jobs from the queue, applies validation and transformation logic, and writes normalized records to PostgreSQL. Failures are retried with backoff; jobs that exhaust retries move to a dead-letter queue for inspection instead of being silently dropped.

Incoming data from an external API is accepted by the integration gateway, given a normalized job payload, and enqueued. A worker later dequeues the job, validates and transforms the payload, and writes it to PostgreSQL. If a downstream call or validation step fails, the job is retried according to a backoff policy; after repeated failure it is routed to a dead-letter queue rather than blocking the queue or being lost.

Retry with backoff, circuit breakers around flaky external dependencies, and dead-letter queues for jobs that exhaust retries together raised integration uptime from roughly 95% to 99.5% and reduced mean time to resolution by about 40%.

Moving processing to a worker pool that could scale independently of the ingestion path roughly doubled data throughput, and reduced API response times by about 25% by taking slow downstream work out of the request/response cycle.

Inbound integration traffic is authenticated at the gateway, and credentials for outbound third-party calls are scoped and stored separately from application configuration.

The queue and worker fleet run as independently scalable services, allowing worker concurrency to be tuned per integration based on that provider's rate limits.

Queue depth, job failure rates, and dead-letter queue volume are tracked as leading indicators of integration health, enabling faster detection and diagnosis of degraded third-party dependencies.

• Data throughput improved approximately 2x.
• Manual data handling reduced by approximately 30%.
• API response times improved by approximately 25%.
• Integration uptime improved from approximately 95% to 99.5%.
• Mean time to resolution (MTTR) reduced by approximately 40%.

• Treating third-party reliability as a first-class design constraint — not an edge case — is what actually moves uptime and MTTR numbers.
• A dead-letter queue is as much an operational tool as an engineering one: it turns failures into inspectable, replayable work instead of silent data loss.
• Per-integration circuit breakers and concurrency limits matter more than global throughput tuning when providers have very different rate limits.