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 (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 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. Requirements 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 BigQuery, 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. #J-18808-Ljbffr