Ontology Engineer

The Global Purchasing Business Analytics Team at Stellantis is seeking a Ontology Engineer to build and maintain robust backend data solutions in support of global purchasing analytics. This role is focused on designing scalable data models and developing high-quality, production-grade code in SQL and Python to support our enterprise analytics architecture. You will work across platforms such as Snowflake, Palantir Foundry, Power BI, and potentially Databricks, helping the team evolve toward advanced capabilities, including AI-enhanced insights. While you’ll collaborate closely with analysts and business stakeholders, this is a data engineering position — not a dashboard/reporting development role. As Stellantis operates globally with diverse and region-specific systems, you will often need to integrate fragmented data sources and iteratively develop solutions without complete documentation or available SMEs. Success in this role requires initiative, curiosity, and a self-starting mindset — someone who can function as a data entrepreneur: discovering and connecting data across domains to drive business value in a complex enterprise environment. You will work on a globally distributed team. Working hours must include at least 4 hours of overlap with Eastern Standard Time (EST) to ensure strong collaboration.

Key Responsibilities include but not limited to:

• Develop, optimize, and maintain SQL / PySpark-based data models to support analytical applications in Global Purchasing
• Partner with data analysts to understand analytical requirements and translate them into well-structured backend data solutions
• Implement data validation, transformation, and automation logic to ensure high data quality
  Write clean, efficient, and maintainable code using SQL, Python/PySpark, and TypeScript (where applicable)
• Explore and connect new or unfamiliar datasets in an iterative and self-directed manner, especially where formal documentation or SMEs are unavailable.
• Collaborate with cross-functional teams to connect complex data into usable data models.
• Contribute to the adoption of AI-driven tooling and workflows where applicable