Data Engineer -IT

Stellantis is a leading global automaker and mobility provider that offers clean, connected, affordable and safe mobility solutions. Our strength lies in the breadth of our iconic brand portfolio, the diversity and passion of our people, and our deep roots in the communities in which we operate. Our ambitious electrification and software strategies and the creation of an innovative ecosystem of strategic, game‑changing partnerships are driving our transformation to a sustainable mobility tech company. With industrial operations in nearly 30 countries, Stellantis consistently exceeds the evolving needs and expectations of consumers in more than 130 markets, while creating superior value for all stakeholders.
The Global Analytics & Data Products Team is looking for a Data Engineer to establish the organization's data infrastructure, promote engineering best practices on data management, and maintain a high bar for data quality.
Design and implement secure, scalable, high‑performance and robust data services for connected vehicle data in distributed data processing platforms using modern Big Data technologies.
Design, extend, and review data architecture, model, flow, and integration – be hands on and involved with every stage of the software development life cycle.
Partner with software engineers and data scientists to meet the needs of upstream and downstream dependencies.
Work with external carrier stakeholders and internal partner teams to ingest connectivity data for data processing.
Keep up to date with evolving Big Data technology, share knowledge across the organization by enabling best practices, standards, governed processes and relevant technologies.
BA/BSc in Computer Science, Engineering, Mathematics, or a related technical discipline.

• 2+ years of experience in data engineering and the software development life cycle.
• 2+ years of hands‑on experience building and maintaining production data applications, experience with relational and distributed columnar data stores.
• Experience using SQL, Python, and Spark.
• Experience with cloud Big Data platforms (e.g., AWS Redshift, Google BigQuery, Azure Data Warehouse).
• Familiarity with time‑series databases, data streaming applications, Kafka, Flink, and more.