Data Engineering for Logistics: Pipelines That Reconcile to the Settlement Run

A SaaS data pipeline ingests JSON and writes to a warehouse. A logistics data pipeline has to ingest load events from the TMS, inventory events from the WMS, GPS and HOS from telematics, EDI documents from 200 trading partners each with their own quirks, fuel transactions from the fuel card provider, and general ledger entries from the ERP — and all of it has to reconcile to the weekly settlement run. Every stream has its own quirks: the TMS doesn't emit proper events, so CDC requires a workaround. The telematics provider's API changed and nobody told the integration team. One customer's EDI 214 shipment status uses a non-standard code. The fuel card feed fails intermittently. And it all has to hold up during peak season when volume triples.

Logistics data engineering done right uses the medallion pattern with extras. Bronze layer ingests each source in its native format. Silver applies EDI normalization, telematics tag standardization, and load-level joining across systems. Gold provides the business-ready views that analytics and Power BI consume. Reconciliation jobs run after every load and surface variances against the settlement run before anyone trusts the gold layer for operational decisions. Monitoring catches the API change that the telematics vendor didn't announce. This discipline is what separates logistics data pipelines that hold up from the ones that break in month three.