Data Engineering Manager

PRO 360 requires a Data Engineering Manager to design, develop, and maintain the enterprise data architecture that unifies commercial customer data across Vehicles, Service, Software, and Ford Credit.

• Lead Data Engineering: Drive the development of real-time and batch data pipelines, manage GCP infrastructure, ensure code quality, and oversee integrations with downstream systems like Salesforce and Marketing Cloud.
• Drive AI Data Initiatives: Architect data foundations to support AI monetization, including predictive analytics and cutting-edge Agentic AI workflows leveraging Google Vertex AI and Gemini.
• Enforce Data Governance: Implement "Policy as Code," machine-readable data contracts, data quality observability, and strict privacy controls (GDPR, CCPA, PRO ID management).

Why This Role Matters As the definitive source of truth for Ford Pro’s commercial customers, PRO 360 is the engine behind Ford Pro’s growth and efficiency goals. This LL6 leadership role ensures that our data is scalable, AI-ready, and highly governed—directly enabling Ford Pro’s commercial success.

Job Responsibilities

Data Engineering:

• Lead Engineering Execution: Manage and mentor pods of data and software engineers to design, build, and deploy domain-driven data products on Google Cloud Platform (BigQuery, Dataflow, Pub/Sub, Cloud Composer/Airflow).
• Platform Modernization: Drive critical infrastructure initiatives, including the migration to GCP 3.0, adoption of DataOps packages, and the decommissioning of legacy tech debt to ensure highly performant and cost-optimized cloud operations.
• Ecosystem Integration: Architect real-time and batch data pipelines to ingest fragmented data and serve unified profiles to downstream operational systems, specifically Salesforce (Sales/Service Cloud), Marketing Cloud, and Ford Credit billing systems.
• Engineering Craftsmanship: Enforce rigorous engineering standards, ensuring 100% of PRO 360 repositories maintain SonarQube "A" ratings for reliability, security, and maintainability, and championing CI/CD automation.
• Technical Leadership: Act as the Directly Responsible Individual (DRI) for technical deployments, collaborating with Product Managers and Product Anchors to translate business OKRs into scalable technical backlogs.

AI Initiatives:

• AI Data Readiness: Architect and optimize data models to support high-priority machine learning initiatives ensuring training and inference pipelines are highly available and scalable.
• Agentic AI Enablement: Lead the data integration strategy for next-generation Agentic AI workflows (using Vertex AI, Gemini, and Agent Platforms), enabling autonomous lead generation, pipeline observability, and conversational AI dashboards.
• Feature Engineering & ML Ops: Collaborate closely with Data Scientists and AI Engineers to transition ML models from proof-of-concept to production, ensuring seamless integration into the PRO 360 ecosystem.
• Unstructured Data & RAG: Build pipelines to process and structure complex datasets (e.g., telematics, connected vehicle data, unstructured web leads) to feed into Large Language Models and Retrieval-Augmented Generation (RAG) frameworks.

Data Governance, Quality & Compliance:

• Data Contracts & Observability: Implement machine-readable data contracts (Schema, SLOs, and DQ rules) for top PRO 360 data products. Oversee automated data quality monitoring and anomaly detection using platform observability tools.
• Privacy & Compliance Controls: Architect and develop automated governance controls to map data assets to privacy classifications. Ensure strict adherence to GDPR and CCPA, including the automated management and suppression/deletion of consent data.
• Policy as Code: Translate business and regulatory policies into enforceable, automated standards within the CI/CD pipeline, eliminating manual configuration errors.
• Federated Data Sharing: Manage the governance of sharing PRO 360 data with internal pillars (FCSD, FPI, FMCC) and external partners (e.g., D&B, S&P) through secure, role-based, and attribute-based access controls.

Bachelor’s / Masters’s Degree in Computer Science, Data Engineering, Information Technology, or a related technical field

Required Qualifications

• 10+ years of hands-on experience in Data Engineering, Data Architecture, or AI/ML Ops, with 3+ years in a technical leadership