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.

At Ford Motor Company, we believe freedom of movement drives human progress. With our incredible plans for the future of mobility, we have a wide variety of opportunities for you to accelerate your career and help us define tomorrow's transportation.

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) to deploy data models, code, and infrastructure.

• Domain Familiarity: Practical familiarity with Integrated Services and Subscriptions platforms (such as subscription billing lifecycles, user entitlement systems, usage-based billing, and digital customer journeys).

Preferred Qualifications:

• Education: Master of Science (M.S.) in Computer Science, Data Engineering, or a highly quantitative field is a strong plus.

• Certifications:

• GCP Professional Data Engineer Certification is a plus.

• GCP Professional Cloud Architect or equivalent Cloud Developer Certifications are a plus.

• Strong proficiency in modern programming languages (Python is required; Scala, Java, or Go is a plus).

• Experience with Infrastructure as Code (IaC) tools, particularly Terraform, for managing cloud infrastructure.

• Hands-on experience with containerization and orchestration platforms, including Docker and Kubernetes (GKE).

• Solid understanding of relational and NoSQL databases, data warehousing concepts, and data lakehouse architectures (e.g., Delta Lake, Iceberg).

• Excellent analytical, problem-solving, and debugging skills.

• Outstanding communication skills, with the ability to articulate complex technical architectures to both technical and non-technical stakeholders.

You may not check every box, or your experience may look a little different from what we''ve outlined, but if you think you can bring value to Ford Motor Company, we encourage you to apply!

As an established global company, we offer the benefit of choice. You can choose what your Ford future will look like: will your story span the globe, or keep you close to home? Will your career be a deep dive into what you love, or a series of new teams and new skills? Will you be a leader, a changemaker, a technical expert, a culture builder…or all of the above? No matter what you choose, we offer a work life that works for you, including:

• Immediate medical, dental, vision and prescription drug coverage
• Flexible family care days, paid parental leave, new parent ramp-up programs, subsidized back-up childcare and more
• Family building benefits including adoption and surrogacy expense reimbursement, fertility treatments, and more
• Vehicle discount program for employees and family members and management leases
• Tuition assistance
• Established and active employee resource groups
• Paid time off for individual and team community service
• A generous schedule of paid holidays, including the week between Christmas and New Year's Day
• Paid time off and the option to purchase additional vacation time.

This position is a salary grade 7 and ranges from $99,600-$166,600.

Final determination of salary grade will be based on candidate''s skills and experience, and base salary will be set within the applicable range according to job scope, responsibility and competitive market value.

For more information on salary and benefits, click here: https://fordcareers.co/GSR

Visa sponsorship is available for this position.

Domestic relocation is not available for this position.

Candidates for positions with Ford Motor Company must be legally authorized to work in the United States. Verification of employment eligibility will be required at the time of hire.

We are an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, religion, color, age, sex, national origin, sexual orientation, gender identity, disability status or protected veteran status. In the United States, if you need a reasonable accommodation for the online application process due to a disability, please call 1-888-336-0660.

This position is hybrid with a requirement to be onsite four or more days per week.

#LI-Hybrid

#LI-PW1

• 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.
• 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.