Data Engineer - Supply Chain

Stellantis; Auburn Hills (MI); On-site; USD 100,000 - 130,000; Full time

Job summary

Stellantis in Auburn Hills is seeking a Data Engineer to play a crucial role in building AI-enabled supply chains. This position involves designing and operating scalable data pipelines for advanced analytics, optimization, and AI solutions.

The ideal candidate will possess over 8 years of experience in data engineering, strong skills in Python and SQL, and familiarity with modern data platforms like Databricks and Snowflake. Join us to contribute to innovative data solutions!

Qualifications

• 8+ years of professional experience in data engineering, analytics engineering, or data platform development.
• Strong proficiency in Python and SQL for data transformation and pipeline development.
• Experience designing and maintaining production-grade data pipelines and analytical data models.

Responsibilities

• Design and maintain scalable data pipelines supporting supply chain analytics.
• Transform and curate data from enterprise systems.
• Implement data quality monitoring and validation.

Skills

Python

SQL

Data pipeline development

Data quality

Data modeling

Collaboration with analytics and AI teams

Education

Bachelor's in Computer Science, Information Systems, or related field

Tools

Databricks

Spark

Snowflake

Job description

We are building an AI-enabled supply chain that senses, predicts, prescribes, and acts. The Data Engineer plays a critical role in enabling this vision by designing, building, and operating enterprise-grade data pipelines and analytical data products that power advanced analytics, optimization, automation, and agentic AI solutions across the Supply Chain organization.

This role focuses on production-ready data engineering-ensuring data is reliable, governed, scalable, and fit for decisioning. The Data Engineer partners closely with Data Science, AI Engineering, Automation, and Platform teams to deliver high-quality data assets embedded into operational workflows.

Responsibilities

• Design, build, and maintain scalable batch and near-real-time data pipelines supporting supply chain analytics and AI use cases
• Ingest, transform, and curate data from enterprise and operational systems (ERP, planning, logistics, manufacturing, execution platforms)
• Develop and maintain analytical data models and feature-ready datasets to support data science, optimization, and agentic AI workflows
• Implement data quality validation, monitoring, and alerting to ensure trust and reliability of downstream analytics
• Optimize data pipelines and storage for performance, cost, and scalability
• Partner with data scientists and AI engineers to support model training, scoring, and deployment needs
• Establish and follow best practices for data modeling, naming conventions, version control, and documentation
• Ensure data solutions comply with enterprise standards for security, privacy, lineage, and governance
• Support production operations, including incident investigation and root cause analysis related to data issues

Basic Qualifications

• Bachelor's in Computer Science, Information Systems, or a related field required
• 8+ years of professional experience in data engineering, analytics engineering, or data platform development
• Strong proficiency in Python and SQL for data transformation and pipeline development
• Experience designing and maintaining production-grade data pipelines and analytical data models
• Hands-on experience with modern data platforms such as Databricks, Spark, Snowflake, or equivalent
• Solid understanding of data quality, validation, and monitoring concepts
• Experience working with structured and semi-structured data at scale
• Proven ability to own production data pipelines end-to-end (design deployment monitoring incident response)
• Demonstrated ability to operate independently, drive technical decisions, and deliver solutions in ambiguous environments with minimal oversight
• Ability to collaborate effectively with analytics, AI, and software engineering teams

Preferred Qualifications

• Master's Degree
• Experience supporting machine learning or advanced analytics pipelines, including feature engineering and model scoring data
• Experience with orchestration tools, CI/CD, and version control for data pipelines
• Familiarity with streaming or event-driven data architectures
• Experience working with supply chain, operations, manufacturing, or ERP data
• Knowledge of data governance, metadata management, and lineage tools
• Experience supporting BI or downstream analytics tools (e.g., Power BI) and enterprise data platforms (e.g., Palantir Foundry)

At Stellantis, we assess candidates based on qualifications, merit, and business needs. We welcome applications from all people without regard to sex, age, ethnicity, nationality, religion, sexual orientation, disability, or any characteristic protected by law. We believe that diverse teams reflect our identity as a global company, enabling us to better address the evolving needs of our customers and care for our future.