Data Engineer - Senior

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July 31

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Cummins Inc.

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Energy • Transport • Hardware

Cummins Inc. is a global power technology leader that designs, manufactures, and distributes a variety of engines and power systems solutions. They offer products that range from diesel and natural gas engines to hybrid and electric power systems, as well as components like turbochargers, fuel systems, and emissions solutions. With a strong emphasis on innovation, Cummins aims to reduce emissions and improve fuel efficiency. The company is dedicated to helping industries navigate the transition to cleaner energy through integrated power solutions suitable for diverse applications such as on-highway, marine, mining, and construction. Additionally, Cummins provides services including remote monitoring, diagnostics, and aftermarket support, reinforcing its commitment to sustainability and customer service excellence.

10,000+ employees

Founded 1919

⚡ Energy

🚗 Transport

🔧 Hardware

💰 $75M Grant on 2024-07

📋 Description

• Leads projects for design, development and maintenance of a data and analytics platform. • Effectively and efficiently process, store and make data available to analysts and other consumers. • Works with key business stakeholders, IT experts and subject-matter experts to plan, design and deliver optimal analytics and data science solutions. • Works on one or many product teams at a time.

🎯 Requirements

• 1) Work closely with business Product Owner to understand product vision. • 2) Play a key role across DBU Data & Analytics Power Cells to define, develop data pipelines for efficient data transport into Cummins Digital Core ( Azure DataLake, Snowflake). • 3) Collaborate closely with AAI Digital Core and AAI Solutions Architecture to ensure alignment of DBU project data pipeline design standards. • 4) Independently design, develop, test, implement complex data pipelines from transactional systems (ERP, CRM) to Datawarehouses, DataLake. • 5) Responsible for creation, maintenence and management of DBU Data & Analytics data engineering documentation and standard operating procedures (SOP). • 6) Take part in evaluation of new data tools, POCs and provide suggestions. • 7) Take full ownership of the developed data pipelines, providing ongoing support for enhancements and performance optimization. • 8) Proactively address and resolve issues that compromise data accuracy and usability.