Senior Data Engineer, Integrated Services Data Warehouse (ISDW)

We made history and now we work to transform the future – for our customers, our communities and our families. You'll see your work on the road every day, helping people move freely and pursue their dreams. At Ford, you can build more than vehicles. Come build what matters.

Do you believe data tells the real story? We do! Redefining mobility requires quality data, metrics and analytics, as well as insightful interpreters and analysts. That's where Global Data Insight & Analytics makes an impact. We advise leadership on business conditions, customer needs and the competitive landscape. With our support, key decision makers can act in meaningful, positive ways. Join us and use your data expertise and analytical skills to drive evidence-based, timely decision making.

We are seeking an exceptional, highly experienced Senior Data Engineer to join our team. In this role, you will design, build, and scale our next-generation big data infrastructure on Google Cloud Platform (GCP) and integrate state-of-the-art Agentic AI systems. You will play a pivotal role in enabling real-time data streaming, advanced analytics, and automated decision-making pipelines that directly influence our global subscription and integrated services products.

If you are passionate about high-performance computing, distributed architectures, and building intelligent agents that operate on massive datasets, we want to hear from you.

• Data Architecture & Pipeline Engineering: Architect, construct, and optimize highly scalable, reliable, and secure end-to-end data ingestion, processing, and distribution pipelines.
• Distributed Computing: Leverage PySpark and Google Cloud technologies to process terabyte-to-petabyte-scale datasets, ensuring optimal execution performance and cost-efficiency.
• Agentic AI & LLM Integration: Design, deploy, and maintain robust Agentic AI solutions (including LLM-based autonomous agents, retrieval-augmented generation (RAG) systems, vector database integrations, and automated tool-use pipelines) that run on top of enterprise data assets.
• CI/CD & DevOps Automation: Establish, maintain, and advocate for modern CI/CD practices across data engineering pipelines, ensuring automated testing, integration, and continuous deployment of data code and ML/AI models.
• Subscription Platform Enablement: Partner with product and platform teams to design data models supporting complex subscription metrics, recurring billing, customer entitlements, metered usage, and churn predictive analytics.
• Collaboration & Leadership: Collaborate closely with Data Scientists, AI Researchers, Product Managers, and Software Engineers to align data architectures with strategic business objectives. Mentoring and guiding junior engineers in technical best practices.
• Data Governance & Security: Champion enterprise-grade data security, regulatory compliance (e.g., GDPR, CCPA), and data quality monitoring across all platform components.

Minimum Qualifications:

• Education: Bachelor’s Degree in Computer Science, Information Technology, Software Engineering, or a closely related engineering field.
• Big Data Platform Experience: 7+ years of hands-on experience in building, managing, and scaling enterprise-grade Big Data platforms and distributed data systems.
• PySpark Mastery: 6+ years of strong, hands-on experience writing production-grade PySpark code, with deep knowledge of Spark optimization techniques, partition tuning, memory management, and debugging.
• Google Cloud Platform (GCP): 3+ years of experience architecting and implementing data solutions on GCP, with deep technical proficiency in: 
  • BigQuery: Advanced analytical SQL, partitioning, clustering, and performance optimization.
  • Dataflow & DataProc: Managed Apache Beam pipelines and Spark/Hadoop clusters.
  • Spanner: High-availability relational database workloads.
  • Astronomer / Apache Airflow: Sophisticated DAG orchestration and workflow scheduling.
• Agentic AI Engineering: Hands-on experience building and deploying Agentic AI solutions (e.g., autonomous AI agents, multi-agent orchestrations, tools integration with LLMs, prompt engineering frameworks, and cognitive search applications).
• CI/CD Pipelines: Hands-on experience building, maintaining, and automating robust CI/CD pipelines (utilizing tools such as GitHub Actions, GitLab CI, Tekton, Jenkins, or equivalent) t