Fleet Data Manager (Fixed-term contract),'s Procurement and Capacity Planning
Listed on 2026-08-12
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Supply Chain/Logistics
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IT/Tech
Description
Amazon's Fleet Procurement and Capacity Planning (FPAC) team manages a large-scale international commercial vehicle fleet across 10 European markets. This fleet depends on accurate, timely data flowing between two Fleet Management Companies (FMCs), Amazon's internal planning systems, and multiple cross-functional stakeholders.
This role owns the operational delivery and quality of all fleet data provided by FMCs (Ayvens and Arval) to Amazon. You will be the single-threaded owner ensuring that vehicle lifecycle data (from order placement through deployment, maintenance, defleeting, and remarketing) is delivered per contractual SLAs, meets validation standards, and flows reliably into the planning tools and reporting mechanisms that drive fleet capacity decisions.
This is a hands-on data operations and vendor management role. You will spend the majority of your time ensuring data reliability, resolving reporting failures with FMC technical contacts, and supporting the internal teams who depend on this data to make procurement and capacity decisions worth hundreds of millions in OPEX annually.
Please note that this is a 12 months fixed-term contract.
Key job responsibilities- Bachelor's degree or equivalent
- Experience working with data analytics and using these metrics to identify problems, or experience in an operational role
- Experience in vendor management, or experience in operations and on-call support for data center facilities, mission critical plants, or production facilities
- Experience performing data analysis and troubleshooting data integrity issues
- Experience in transportation or supply chain within a high-volume logistics, manufacturing or engineering environment
- Experience working within a global company, with multi-country responsibilities
- Experience in procurement, supply chain, inventory management, contract management, lease administration or operations
- Experience managing data pipelines
- Experience with machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance
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