Industry 4.0 Data Engineer

Industry 4.0 Data Engineer Driving the future of automotive. We’re Lear For You. Company Overview Lear, a global automotive technology leader in Seating and E-Systems, enables superior in-vehicle experiences for consumers around the world. Our diverse team of more than 165,000 talented employees in 37 countries is driven by a commitment to innovation, operational excellence, and sustainability. Lear is Making every drive better™ by providing the technology for safer, smarter, and more comfortable journeys. Lear, headquartered in Southfield, Michigan, serves every major automaker in the world and ranks 186 on the Fortune 500. Further information about Lear is available at lear.com, or follow us on Twitter LearCorporation Position overview: As a data engineer you will be working with colleagues focused on understanding the operational Use Cases and Data scientists using ML models to deliver solutions to support business decisions. As a data engineer you will be responsible for the development of the data pipelines, their security, reliability and efficiency; supporting industry 4.0 projects and the rollout of activities that are identified. Key Responsibilities: Manage the execution of data-focused projects using Lear’s data analytics and application platform as a member of the EU&AF AME team. Participate in multiple projects from conception to root cause analytics and solution deployment. Support the creation of a user toolset to be deployed as a regional standard across Lear for analyzing and presenting data effectively to senior management. Generate large databases from various sources of both structured and unstructured data. Utilize a range of programming languages to transform big data into manageable datasets. Translate technical data into understandable insights, providing recommendations and conclusions to stakeholders. Understand program and product delivery phases, contributing expert analysis across the lifecycle. Work with both new and legacy technologies to integrate separate data feeds and transform them into new scalable datasets. Ensure documentation and procedures align with internal practices (ITPM) and Sarbanes Oxley requirements, continuously improving them. Optimize system performance for all deployed resources. Key skills and Qualifications: Required: Bachelor’s degree qualification in Computer Science, Electronics or Systems / Software Engineering. Experience working as part of an integrated data team. Strong business analysis/requirements analysis skills. Business Analysis. Working within an Agile framework. Proficiency in English language, spoken and written Preferred: 2- 5 years’ experience of working within a multinational organization with international, cross functional teams. 2- 5 years’ experience of working within data engineering or optimization to support a high volume manufacturing operation. Master’s degree in Computer Science, Systems, or comparable Electronic Discipline. Experience working with data for ML purposes. Key Skills: Expert level of knowledge of data warehousing and/or relational databases. High Level of knowledge of operating with Python and preferably PySpark Platforms. Experience in data mining methods and data tools Experience with cloud-based data platforms. Knowledge of applying data lineage, transformation and quality analysis activities. Competent to reach into data sources to create new data extraction interfaces. Proven experience developing data pipelines and ETL processes from disparate data sources. Why Lear We offer a fantastic place to work, opportunity to grow and being part of a company who places its people first. Lear Corporation is an Equal Opportunity Employer, committed to a diverse workplace. Applicants must submit their resumes for consideration using our applicant tracking system. Due to the high volume of applications received, only candidates selected for interviews will be contacted. Unsolicited resumes from search firms or employment agencies, or similar, will not be paid a fee and will become the property of Lear Corporation.