Director, Data Engineering and Architecture

Ford Ford · Auto · Dearborn, MI +1 · Enterprise Technology

Director of Data Engineering and Architecture at Ford, focused on establishing AI Readiness by defining enterprise standards for AI-ready data, driving platform evolution, and designing data foundations for AI agents and RAG applications. This role requires leadership in data ingestion, transformation, representation, and ensuring scalability, cost-efficiency, and compliance within a GCP environment.

What you'd actually do

  1. Define the Vision for AI Readiness: Establish and document the enterprise standards for AI-Ready data, including semantic integrity, automated metadata tagging, high-fidelity pipelines, etc.
  2. Drive Platform Evolution & Modernization: Proactively identify and champion the adoption of next-generation technologies and cloud-native patterns. Act as a strategic catalyst for transitioning the EDP toward higher-maturity operating models, ensuring Ford remains at the cutting edge of data and AI innovation
  3. Design and Build for Agentic Intelligence: Work cross-functionally within EDP to design and build data for AI agents to interpret without human intervention while ensuring consistency and context across the enterprise.
  4. Collaboration & Leadership: Lead and mentor an organization of Data Architects and Engineers. Working within the Data Platform and Engineering team to ensure solutions are scalable and executable.
  5. Privacy & Compliance by Design: Ensure development patterns enable Ford’s rigorous commitment to data security and global privacy regulations.

Skills

Required

  • 12+ years of experience in Cloud-Based Data Architecture, Engineering or a related field
  • 3+ years of experience leading technology teams
  • GCP Expertise
  • BigQuery, Dataflow, Pub/Sub, and Cloud Storage
  • CI/CD, and site-reliability engineering principles
  • Vector databases
  • LLM orchestration frameworks
  • building data foundations for RAG-based applications
  • data modeling (Relational, Dimensional, Graph)
  • define complex semantic ontologies for large-scale enterprises
  • translate complex architectural concepts into strategic business value for executive leadership

Nice to have

  • Bachelor’s degree in Computer Science, Informatics, Data Engineering, or a related field

What the JD emphasized

  • AI Readiness
  • AI agents
  • RAG-based applications
  • enterprise standards
  • data for AI agents to interpret without human intervention
  • rigorous commitment to data security and global privacy regulations

Other signals

  • establishing engineering excellence
  • architectural standards
  • runbooks
  • skills that all teams can leverage
  • critical enabler
  • leader who can navigate the complexities of our enterprise-scale data
  • defining how data is ingested, transformed, and represented
  • build a platform that is self-healing, cost-efficient, and capable of supporting autonomous systems that can query, reason, and execute tasks
  • strategic thinker
  • semantic ontologies
  • distributed systems
  • organizational change
  • Define the Vision for AI Readiness
  • enterprise standards for AI-Ready data
  • semantic integrity
  • automated metadata tagging
  • high-fidelity pipelines
  • Drive Platform Evolution & Modernization
  • next-generation technologies
  • cloud-native patterns
  • higher-maturity operating models
  • data and AI innovation
  • Design and Build for Agentic Intelligence
  • design and build data for AI agents to interpret without human intervention
  • consistency and context across the enterprise
  • Collaboration & Leadership
  • lead and mentor an organization of Data Architects and Engineers
  • ensure solutions are scalable and executable
  • Privacy & Compliance by Design
  • rigorous commitment to data security and global privacy regulations
  • 12+ years of experience in Cloud-Based Data Architecture, Engineering or a related field
  • experience in a leadership role
  • 3+ years of experience leading technology teams
  • GCP Expertise
  • Deep technical proficiency in the Google Cloud Platform ecosystem
  • BigQuery, Dataflow, Pub/Sub, and Cloud Storage
  • Data Operations Background
  • Understand CI/CD, and site-reliability engineering principles
  • AI/ML Foundations
  • Strong understanding of modern AI requirements
  • Vector databases
  • LLM orchestration frameworks
  • building data foundations for RAG-based applications
  • Strategic Modeling
  • Expert-level knowledge of data modeling (Relational, Dimensional, Graph)
  • define complex semantic ontologies for large-scale enterprises
  • Communication
  • translate complex architectural concepts into strategic business value for executive leadership