Data Engineering Manager

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 (Big Query, Dataflow, Pub/Sub, Cloud Composer/Airflow).
• Platform Modernization:
  Drive critical infrastructure initiatives, including the migration to GCP 3.0, adoption of Data Ops 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 Sonar Qube "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 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 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.

• 10+ years of hands‑on experience in Data Engineering, Data Architecture, or AI/ML Ops, with 3+ years in a technical leadership role.
• Deep technical proficiency in Google Cloud Platform (GCP) data services, including Big Query, Cloud Composer, Dataflow, Pub/Sub, and Dataplex.
• Proven track record of operationalizing AI/ML models and supporting GenAI/Agentic AI infrastructure (Vertex AI, LLM orchestration).
• Strong programming skills in Python and SQL, with proficiency in Terraform/IaC for infrastructure automation.
• Experience building and scaling Customer Data Platforms (CDPs), Master Data Management (MDM) solutions, or complex B2B data ecosystems.
• Familiarity with CRM integrations (specifically Salesforce) and enterprise billing/financial data flows.
• Strong understanding of enterprise data governance, data contracts, and global privacy regulations (GDPR, CCPA).
• Excellent communication skills with the ability to translate complex technical architectures to business stakeholders and executive leadership.