Staff Data Platform and Products Architect

Ford • Dearborn, MI

17h • $132,800 - $250,800 • Hybrid

About The Position

Ford’s Electric Vehicles, Digital and Design (EVDD) team is charged with delivering the company’s vision of a fully electric transportation future. EVDD is customer-obsessed, entrepreneurial, and data-driven and is dedicated to delivering industry-leading customer experience for electric vehicle buyers and owners. You’ll join an agile team of doers pioneering our EV future by working collaboratively, staying focused on only what matters, and delivering excellence day in and day out. Join us to make positive change by helping build a better world where every person is free to move and pursue their dreams. In this role... At Ford, we are transforming our data ecosystem to operate with a modern Data paradigm, treating data and platform capabilities as first-class, secure, and versioned products. As a Staff Data Platform and Products Architect, you will design the foundation that empowers data engineers, data scientists, and application developers to easily ingest, transform, and securely consume high-quality data. In this high-impact, individual contributor role, you will lead the transition toward a product-centric data architecture. You will be responsible for defining the technical roadmap and establishing the architectural guardrails for our next-generation cloud infrastructure. This role requires an architect who can navigate complex technical infrastructure—including event-driven architectures, real-time streaming, API development, containerized microservices, schema-first governance, and automated GitOps/CI/CD—while championing developer experience (DevEx) and keeping platform decisions aligned with strategic business priorities. If you are passionate about building highly optimized developer platforms and driving technical excellence, this is the role for you.

Requirements

• Bachelor’s degree in Computer Science, Data Engineering, Information Technology, or a related technical field (or equivalent practical experience).
• 7+ years of progressive technical experience in data architecture, platform engineering, or enterprise-scale data engineering.
• 3+ years of experience designing and operating end-to-end data platforms on Google Cloud Platform (GCP), AWS, or Azure.
• 3+ years of practical application development experience using Python, SQL, and Java (or Go/Bash) tailored for high-performance pipeline development, API creation, and automation scripting.
• Experience building at least 1 centralized developer ecosystem or developer-facing templating framework using Git submodules or standardized repositories.
• Exceptional communication and presentation skills, with a proven ability to explain complex technical and architectural concepts clearly to both technical engineers and business leaders.
• Must live within reasonable commuting distance of Dearborn, MI to support a hybrid work model.

Nice To Haves

• Master’s degree in Computer Science, Data Science, or equivalent; Google Cloud Professional Data Engineer or Professional Cloud Architect certifications.
• Experience designing, building, and deploying RESTful and gRPC APIs, including schema definition, authorization models, and API gateway integrations.
• Deep expertise in streaming and analytical data modeling (Star Schema, Data Vault) across cloud-native databases (BigQuery, BigTable, PostgreSQL).
• Proven experience building production streaming pipelines with Apache Beam/Dataflow and orchestrating distributed Spark/PySpark processing on Dataproc Serverless (including JVM tuning and performance optimization).
• Hands-on experience with Apache Airflow, including writing robust DAG design patterns and managing multi-environment orchestrator instances.
• Strong experience with GitHub Actions (or Tekton), Terraform, Docker/Buildah, and running workloads on containerized orchestration platforms like Cloud Run or GKE.

Responsibilities

• Architect Scalable Platform Solutions: Design, build, and govern highly performant, distributed data solutions on GCP, leveraging BigQuery, Dataproc, Dataflow, BigTable, Cloud Storage, Cloud Run, Pub/Sub, and Vertex AI to support global analytical, operational, and real-time streaming use cases.
• Lead Cloud Data Integration & Streaming: Design and implement high-volume, event-driven streaming pipelines using GCP Pub/Sub, Apache Beam, and Apache Kafka. Build real-time stream processing systems to ingest and land high-volume events into optimized BigQuery analytical layers and low-latency BigTable operational stores.
• Design & Productize APIs: Architect and implement secure, versioned, and highly performant RESTful and gRPC APIs that expose platform services and data products to internal and external consumers. Define API contracts, versioning strategies, and authentication/authorization patterns (OAuth, mTLS) to enable reliable self-service access.
• Enforce Schema-First Governance: Champion data contract standardizations and shift-left data quality practices. Define and enforce data schemas using Protobuf and centralized Schema Registries, ensuring robust boundaries, backward compatibility, and semantic alignment across distributed producer and consumer teams.
• Build Reusable Platform Frameworks: Design opinionated, extensible frameworks and libraries that abstract underlying infrastructure complexity. Enable product engineering teams to easily bootstrap and scaffold production-ready data pipelines and microservices through standardized templates, configuration-driven workflows, and automated code generation using Python and Jinja templating.
• Drive Platform and Data Strategy: Collaborate with business, product, and engineering leaders to translate organizational objectives into executable architectural roadmaps. Establish clear, clean boundaries between core platform responsibilities and feature-team domain boundaries.
• Automate Infrastructure-as-Code (IaC): Lead the GitOps lifecycle by designing and maintaining Terraform (HCL) configurations and automated CI/CD pipelines (GitHub Actions, Tekton) to ensure consistent, secure, and rapid infrastructure deployments across Dev, QA, and Production environments.
• Model and Transform Data: Design and govern modern data modeling patterns (such as dimensional modeling, Data Vault, and event-based streaming schemas) across analytical tables, relational databases, and flat-file stores. Orchestrate distributed Spark batch processing, complex SQL transformations, and ML training pipelines using Apache Airflow.
• Operate Containerized Services: Build, publish, and secure container images using Buildah and Docker, managing deployments to Cloud Run and Google Kubernetes Engine (GKE). Automate container image promotion, environment progression, and artifact registry security through dedicated CI/CD pipelines.
• Technical Leadership & Enablement: Provide hands-on mentorship and architectural guidance to data engineering teams. Establish best practices, design patterns, and style guides, while building developer-enablement tooling that minimizes onboarding friction and accelerates time-to-market.

Benefits

• Immediate medical, dental, vision and prescription drug coverage
• Flexible family care days, paid parental leave, new parent ramp-up programs, subsidized back-up child care 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior