Data Engineering Manager

Job Responsibilities

• 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.