Data Engineering for Transportation: Telematics, TMS, and Operations Pipelines

Transportation data engineering navigates source systems and data patterns most enterprise data engineers haven't seen. Fleet telematics (Samsara, Geotab, Motive, Omnitracs) produces high-frequency time-series data at fleet scale. TMS platforms (McLeod, TMW, MercuryGate for trucking; Sabre for airlines; CargoWise for ocean) hold load, flight, and voyage data with industry-specific models. Dispatch systems hold real-time asset and driver assignments. MRO systems (AMOS for airlines, Wabtec for rail, ShipNet for maritime) hold maintenance records with asset-specific compliance tracking. Aircraft and locomotive data includes engine parameters, cycles, and flight/operating hours that drive MRO schedules. Assets have identifiers that vary by system — VIN, tail number, locomotive number, IMO number for vessels — and none of them match across TMS, telematics, MRO, and financial systems without asset master data. Generic enterprise data engineering addresses none of this.

Transportation data engineering that works follows industry-specific patterns. Asset master data reconciling VIN, tail number, locomotive number, IMO number across TMS, telematics, MRO, and financial systems. Telematics ingestion with time-series database patterns at fleet scale. TMS integration with load/flight/voyage dimensional structure. Dispatch integration with the real-time discipline operations requires. MRO integration with the compliance tracking FAA Part 121 or FRA mandates. HOS and ELD data preservation under 49 CFR Part 395 retention. PTC data under FRA expectations. Bronze-silver-gold medallion with gold models aligned to fleet operations, safety, commercial, and regulatory use cases. Done with this discipline, the platform supports the full analytics stack. Done generically, it produces data that fails DOT, FAA, or FRA examination.