Data Pipelines & Integration
We build the plumbing that moves data between systems reliably, on time, and in the right shape.
Expertise, applied
Data pipelines are unsexy infrastructure work until they break. Then they're the reason your month-end close is late, your compliance report has errors, or your dashboard shows numbers from yesterday. We build pipelines that are observable, recoverable, and testable—not just functional.
For real-time workloads, we use Kafka or AWS Kinesis for event streaming with exactly-once delivery guarantees. Go services consume and transform events with sub-second latency. For batch and ELT workloads, Airflow orchestrates extraction from source systems, dbt handles transformation and testing in the warehouse, and TimescaleDB stores time-series data when PostgreSQL's native partitioning isn't enough.
Every pipeline we build ships with schema validation at ingestion, dead-letter queues for malformed records, idempotent processing for safe retries, and monitoring that alerts on lag, throughput drops, and schema drift. We've seen too many pipelines that work fine until someone upstream changes a column name and the whole thing silently drops records for a week.