Data Engineer- Full Stack

Ford Motor Company
8h

About The Position

As a Senior Data Engineer, you will architect and scale end-to-end data pipelines on GCP, transforming complex telemetry and enterprise data into high-quality, analytics-ready assets using Medallion architectures. You will lead the implementation of robust CI/CD workflows, rigorous data governance, and security controls while mentoring junior talent and driving engineering best practices. By collaborating with cross-functional stakeholders and optimizing cloud performance, you will ensure the data platform remains secure, cost-effective, and highly available to power critical business insights. Operational Excellence: Using Terraform, Git, and Airflow to ensure reproducible, secure, and cost-optimized cloud infrastructure. Governance & Quality: Prioritizing data lineage, PII protection, and observability to maintain high trust in data assets. Collaboration: Acting as a bridge between technical teams (Data Science, Security) and business stakeholders to deliver self-service analytics. Mentorship: Elevating the team through code reviews and documentation standards. Technical Leadership: Designing end-to-end ETL/ELT pipelines and migrating assets to centralized platforms like BigQuery.

Responsibilities

  • Architect and scale end-to-end data pipelines on GCP
  • Transform complex telemetry and enterprise data into high-quality, analytics-ready assets using Medallion architectures
  • Lead the implementation of robust CI/CD workflows, rigorous data governance, and security controls
  • Mentor junior talent and driving engineering best practices
  • Collaborate with cross-functional stakeholders and optimizing cloud performance
  • Ensure the data platform remains secure, cost-effective, and highly available to power critical business insights
  • Using Terraform, Git, and Airflow to ensure reproducible, secure, and cost-optimized cloud infrastructure
  • Prioritizing data lineage, PII protection, and observability to maintain high trust in data assets
  • Acting as a bridge between technical teams (Data Science, Security) and business stakeholders to deliver self-service analytics
  • Elevating the team through code reviews and documentation standards
  • Designing end-to-end ETL/ELT pipelines and migrating assets to centralized platforms like BigQuery
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