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Generalist mid-level data role, metro location, and broad stack requirements increase candidate competition.
Core data engineering and BI skills are transferable, though telematics/warranty domain preference raises domain specificity.
Explicit 2–6 years requirement plus mandatory data engineering, BI, and tooling experience increases strictness.
Transform large-scale engineering, warranty, telematics, product, and operational data into actionable insights to improve product quality, business performance, and customer satisfaction.
Develop and maintain real-time dashboards, KPI scorecards, and analytics platforms across global Agricultural product lines to enable strategic and operational decision-making.
Lead AI, Machine Learning, Generative AI initiatives and design scalable data architectures, while supporting digital product development through Power Apps, Databricks, Python, and advanced analytics solutions.
2 to 6 years of work experience in data analysis with statistical tools and providing recommendations.
Experience in Advanced Analytics, Data Engineering, Business Intelligence, and working with Python, Databricks, SQL, Power BI, Power Query, Excel, Power Apps, and Qlik Sense.
Experience handling large-scale structured and unstructured datasets, cloud-based platforms, and data lake environments.
Bachelor’s degree in engineering (Mechanical, Electrical, Computer Science) or similar.
Experience working with telematics, warranty, engineering performance, bill of material, or quality data relevant to agricultural product lines.
Proven track record in leading AI, Machine Learning, and Generative AI projects to drive engineering efficiency and business process improvements.
Capable of collaborating with cross-functional teams (Engineering, Manufacturing, Finance, Product Management, Quality) to align metrics, priorities, and improvements in a global enterprise setting.