Senior Full Stack Software Engineer at Stellantis in Auburn Hills, MI

The Data Governance & Data Management (DGDM) team defines and operationalizes the rules, standards, tools, and applications that make data governance, data management, and data quality real and usable across Stellantis' connected vehicle data ecosystem — spanning data cataloguing, access governance, Compliance by Design, retention enforcement, data extraction for customers, and data quality monitoring.

This is an engineering role at the heart of that mission. While the Data Governance, Data Management, and Data Quality teams define the frameworks, policies, and functional requirements, you are the engineer who turns them into working, reliable, production-grade software — from front-end interfaces to backend services, working with Data Engineers.

Acting transversally across the three DGDM activities — Data Governance, Data Management, and Data Quality — you are the central engineering resource that designs, builds, and maintains the products these teams depend on: the Data Catalogue and Vehicle Signal Metadata Catalogue, the Data Privacy Portal, data extraction pipelines, data quality dashboards, and governance workflow tools.

Key Responsibilities:

• Design, develop, and maintain full-stack applications and services supporting Data Governance, Data Management, and Data Quality use cases (e.g. Data Privacy Portal, Data Catalogue interfaces, DSAR/SAR extraction tooling, data quality monitoring dashboards)
• Build and maintain backend services and APIs (Python, Scala/Spark, Flask) integrated with Databricks (Unity Catalog, Lakebase/Postgres, Delta Lake) and AWS services (Lambda, S3, SES, SQS)
• Develop front-end interfaces and internal tools (React, Streamlit) that make governance, cataloguing, and data quality processes accessible to business and technical users
• Contribute to the technical architecture of DGDM products, ensuring scalability, security, and maintainability across environments
• Translate functional requirements from the Data Governance, Data Management, and Data Quality teams into technical specifications and working software
• Ensure engineering best practices — code quality, testing, CI/CD, documentation, and secure handling of sensitive and personal data
• Collaborate closely with Data Governance Managers, Data Stewards, and Data Quality leads to prioritize and deliver shared product roadmaps
• Provide technical support and troubleshooting for deployed applications, including performance, connectivity, and authentication issues

Basic Qualifications:

• Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field
• A minimum of 5 years of professional software engineering experience, including full-stack development
• Strong proficiency in backend development (Python required; Scala/Spark a plus) and front-end frameworks (React or equivalent)
• Experience with cloud services (AWS, preferred: Lambda, S3, SES, SQS, IAM); Azure/GCP equivalent acceptable
• Exposure to GenAI/LLM-powered application development, AI Agents, etc.
• Solid understanding of data architecture, REST APIs, and system integration patterns
• Experience building internal tools and products (dashboards, portals, workflow applications) for both business and technical stakeholders
• Ability to work autonomously across multiple concurrent initiatives and shifting priorities, in support of several teams at once
• Strong collaboration and communication skills — able to work with non-technical governance and business stakeholders as well as engineering peers
• Experience working in agile, cross-functional environments
• Experience with infrastructure-as-code (Terraform)
• Experience with Databricks (Unity Catalog, Delta Lake, notebooks) is a plus

Preferred Qualifications:

• Experience in the automotive, connected vehicle, or IoT data environment
• Familiarity with data governance, data privacy, or data quality concepts and tools (Collibra, Immuta, Unity Catalog permissions)
• Knowledge of GDPR, EU Data Act, and EU AI Act, and their practical implications on system design