Data Engineering
We build the data infrastructure that makes analytics and AI possible: pipelines that reconcile fragmented source systems, a warehouse that's fast enough to query directly, and dashboards operators actually trust because the numbers stay current.
What we deliver
- Data platform design: ingestion, warehousing, and governance across fragmented source systems
- ETL/ELT pipelines with monitoring and alerting when data quality drifts
- Real-time and near-real-time dashboards for the operational metrics your team checks daily
- Data modeling and semantic layers so the same numbers mean the same thing across every report
- Cost and performance tuning for warehouses that are being asked to do more as data volume grows
Our approach
Assess
Audit source systems, data quality, and the specific decisions the pipeline needs to support.
Architect
Pipeline and warehouse design scoped to freshness and cost requirements, agreed up front.
Build
Incremental delivery — a working pipeline and dashboard early, expanded against real usage.
Operate
Monitoring for pipeline failures and data drift, with a quarterly cost and performance review.
Proof
A regional logistics fleet was reconciling telematics data manually, days after the fact. We built a real-time ingestion pipeline and dashboard surfacing idle time and route inefficiency as it happened — saving an estimated $300k a year in fuel.