The AI Data Architect (m/f/d) is a senior technology leader responsible for defining the data architecture, data structures, and information foundations required to enable AI at scale across the Automotive Business Unit. Reporting to the AI Product Owner and working closely with the AI Architect, Transformation Success Managers (TSMs), Regional IT Directors, ADITRAC, TEIS, OneData, and external partners, this role owns the data architecture framework that makes AI use cases feasible, scalable, and reliable across regions and functions.
This role ensures that AI initiatives are built on governed, accessible, and business-aligned data foundations. The AI Data Architect (m/f/d) will accelerate AI value creation by improving data availability, defining reusable data models, and aligning data sources, structures, and governance with the needs of priority AI use cases. The position blends enterprise data architecture, AI enablement, information governance, and execution support in a complex global manufacturing environment.
Responsibilities
We are seeking a Senior Data and AI leader (m/f/d) with proven experience designing scalable data architecture and enabling AI solutions in large, matrixed, and regulated organizations. The ideal candidate combines strong data architecture depth with practical delivery focus, and has a track record of translating fragmented or complex data landscapes into usable foundations for analytics, automation, and AI.
The ideal candidates will bring experience in:
- enterprise data architecture and information model design
- enabling AI and analytics solutions through data availability and quality
- defining data structures, taxonomies, and reusable models across domains
- aligning business data needs with enterprise platforms, governance, and integration patterns
- working across data, architecture, engineering, security, and business teams
- improving access to trusted, governed, and scalable datasets for digital and AI use cases
Experience in Automotive, manufacturing, or similarly complex global environments is valued, but not necessary. Candidates should be comfortable operating across regions, influencing senior stakeholders, and working at the intersection of business need, technology capability, and governed data enablement.
