Platform Data Engineer - Hybrid, GCP

Ford Motor Company

Dearborn (MI)

Hybrid

USD 68,000 - 193,000

Full time

8 days ago

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Benefits offered by this job

Medical, dental, vision
Parental leave
Vehicle discount
Tuition assistance
Paid time off
Holidays

Job summary

Ford Motor Company in Dearborn, MI is hiring a Platform Software Engineer to join the Enterprise Data Platform team. The role focuses on building scalable data pipelines, microservices, and cloud-native solutions on Google Cloud Platform.

You will collaborate with engineering, operations, and architecture teams, write Java or Python, and apply CI/CD practices to deliver features to production in a hybrid work setup.

Qualifications

  • Bachelor's degree in computer science or related field is required.
  • 3+ years of professional software engineering experience.
  • Experience with cloud platforms (GCP preferred).

Responsibilities

  • Design and build scalable data pipelines and microservices on GCP.
  • Create and maintain SOA and microservices architectures.
  • Integrate front-end and back-end components for robust data access.
  • Lead data ingestion and integration from multiple sources.
  • Leverage GCP services like BigQuery, Dataflow, Pub/Sub, Cloud Functions.
  • Implement data governance and security best practices.
  • Automate platform processes to boost reliability and efficiency.

Skills

Java
Spring Boot
REST APIs
JavaScript
HTML5
CSS3
AngularJS

Education

BS in Computer Science or equivalent
Bachelor's degree in computer science, Data Engineering or related technical field

Tools

SonarQube
CI/CD
Tekton
Terraform
GCS
GCP
Kubernetes

Job description

Ford Motor Company in Dearborn, MI is hiring a Platform Software Engineer to join the Enterprise Data Platform team. The role focuses on building scalable data pipelines, microservices, and cloud-native solutions on Google Cloud Platform.

You will collaborate with engineering, operations, and architecture teams, write Java or Python, and apply CI/CD practices to deliver features to production in a hybrid work setup.

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