Full Stack Data Engineer

Ford Ford · Auto · Dearborn, MI +1 · Global Data Insight & Analytics

Full Stack Data Engineer role at Ford, focusing on designing, developing, and maintaining applications, services, and data solutions for connected vehicle data use cases. The role involves working across backend (Java, Spring Boot, SQL) and frontend (React/Angular, TypeScript) technologies, event-driven architectures (Kafka), and cloud platforms (GCP, OpenShift/Kubernetes). Responsibilities include building APIs, data tools, implementing monitoring, and ensuring data governance. While not core AI development, the role has exposure to AI engineering concepts like RAG, embeddings, and LLM APIs, and encourages the use of AI-assisted development tools.

What you'd actually do

  1. Design, develop, test, deploy, and support full stack applications, APIs, dashboards, and internal data tools.
  2. Build backend services and REST APIs using Java, Spring Boot, SQL, and related technologies.
  3. Contribute to frontend applications using React or Angular, TypeScript, JavaScript, HTML, and CSS.
  4. Support event-driven data flows using Kafka, Pub/Sub, or similar messaging platforms.
  5. Deploy and operate applications on OpenShift, Kubernetes-based platforms, and Google Cloud Platform.

Skills

Required

  • Java
  • Spring Boot
  • SQL
  • React
  • Angular
  • TypeScript
  • JavaScript
  • HTML
  • CSS
  • Kafka
  • Pub/Sub
  • OpenShift
  • Kubernetes
  • Google Cloud Platform
  • BigQuery
  • Cloud Storage
  • Dataflow
  • Cloud Logging
  • Cloud Monitoring
  • CI/CD
  • automated testing
  • Git

Nice to have

  • connected vehicle
  • IoT
  • telemetry
  • streaming
  • large-scale data platform
  • prompt/context engineering
  • RAG patterns
  • embeddings
  • knowledge bases
  • LLM APIs
  • output validation
  • GitHub Copilot
  • ChatGPT
  • Gemini
  • Claude
  • JUnit

What the JD emphasized

  • connected vehicle data
  • data solutions
  • backend
  • frontend
  • cloud-native applications
  • event-driven architectures
  • data engineering principles
  • full stack applications
  • backend services
  • frontend applications
  • event-driven data flows
  • GCP services
  • automated testing
  • CI/CD
  • secure coding
  • API security
  • privacy
  • data governance practices
  • connected vehicle
  • IoT
  • telemetry
  • streaming
  • large-scale data platform
  • AI engineering concepts
  • prompt/context engineering
  • RAG patterns
  • embeddings
  • knowledge bases
  • LLM APIs
  • output validation
  • AI-assisted development tools
  • GitHub Copilot
  • ChatGPT
  • Gemini
  • Claude
  • productivity
  • code quality
  • test generation
  • documentation
  • Git
  • CI/CD pipelines
  • automated testing frameworks
  • problem-solving skills
  • learn across software, cloud, and data engineering areas