Fendt manufactures tractors and agricultural equipment across 10,000+ employees, now hiring heavily into engineering and manufacturing roles—64 positions posted in the last 30 days with velocity accelerating. The tech stack spans CAD/simulation (Creo, MATLAB, STAR-CCM+, Windchill), enterprise systems (SAP, Salesforce), and cloud platforms (AWS, Azure, GCP), with recent adoption of Automotive Ethernet signaling a shift toward connected vehicle capabilities. Active projects focus on field validation, dealer support, and process optimization—consistent with scaling production while maintaining quality and safety systems.
Notable leadership hires: Sales Director, Assembly Planning Lead
Fendt, part of AGCO, designs and manufactures agricultural tractors and equipment for professional farmers across North America, Europe, South America, and Australia. The company operates from its headquarters in Marktoberdorf, Germany, and has been in continuous operation since 1930. Current organizational focus spans engineering, manufacturing, and field support—with active hiring across the United States, Germany, Brazil, Denmark, Australia, and France. Projects underway include field testing, dealer training, lean process development, and simulation validation, reflecting the company's dual focus on product innovation and operational efficiency.
Fendt uses Creo Parametric, AutoCAD, Pro/ENGINEER, MATLAB, Simulink, and STAR-CCM+ for design and engineering analysis, alongside PTC Windchill for product lifecycle management.
Yes. Fendt is currently adopting Automotive Ethernet, indicating a strategic shift toward network connectivity and smart equipment capabilities across its product line.
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Fendt's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.