John Deere operates a sprawling manufacturing and engineering organization across agriculture, construction, forestry, and turf — with 202 active roles and 152 postings in the last 30 days signaling aggressive scaling. The stack is heavily SAP-centric (ECC, S/4HANA, PI, IDoc, SD, MM, HANA, BusinessObjects, Fiori, BTP, Global Trade Services) paired with Databricks, Python, and Tableau for analytics, and CAD/PLM tools (AutoCAD, Creo, Solidworks, Pro/ENGINEER, Windchill) for design and lifecycle management. The hiring mix is engineering-dominant (81 roles) with a long tail of intern (57) and mid-level (57) positions, reflecting both greenfield projects and production ramp demands.
Notable leadership hires: Delivery Lead, Team Lead, Infrastructure Lead
John Deere designs, manufactures, and supports machinery for agriculture, construction, forestry, turf care, and golf operations globally. The company operates a full-stack manufacturing footprint spanning production engineering, supply chain, dealer networks, and field operations across the United States and Europe (Germany, Luxembourg, Netherlands, France, Poland, United Kingdom, Brazil). Core operational challenges revolve around material flow optimization, safety and quality delivery, dealer performance management, and production launch execution. The platform stack integrates heavy ERP (SAP) with modern analytics (Databricks, Tableau, Power BI) and specialized CAD/PLM systems for product design and lifecycle tracking.
John Deere's core stack centers on SAP (ECC, S/4HANA, PI, IDoc, SD, MM, HANA, BusinessObjects, Fiori) for enterprise operations, Databricks and Python for analytics, Tableau and Power BI for visualization, AWS and Azure for cloud, and CAD/PLM tools including AutoCAD, Creo, Solidworks, and Windchill for product design.
John Deere is actively hiring in the United States, Luxembourg, Germany, Netherlands, Brazil, France, Poland, and the United Kingdom, with a hiring velocity marked as accelerating.
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John Deere'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.