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Data Engineer
Job in
Dearborn, Wayne County, Michigan, 48120, USA
Listed on 2026-06-04
Listing for:
Ford Motor Company
Full Time
position Listed on 2026-06-04
Job specializations:
-
IT/Tech
Data Engineer, Data Analyst
Job Description & How to Apply Below
360 team is tasked with creating a seamless, data-driven ecosystem for Ford Pro. In this role, you will be responsible for the end-to-end data lifecycle-from ingestion and transformation to visualization and executive presentation.
You will work within a modern tech stack centered on the Google Cloud Platform, utilizing your expertise in SQL and Python to build scalable pipelines. Uniquely, this role bridges the gap between traditional data engineering and Dev Ops, as you will manage infrastructure using Terraform and Tekton. Beyond the technical build, you will act as a consultant to the business, using Looker Studio and the Microsoft Office suite to present insights that influence strategic decisions at management level.
As a Data Engineer within the Pro
360 division of Ford's Global Data Insight & Analytics (GDI&A) organization, you will play a pivotal role in architecting the data foundations that drive our commercial business forward. This role is designed for a technical expert who excels at building robust data pipelines, managing cloud infrastructure as code, and translating complex datasets into compelling visual narratives for stakeholders and leadership.
* Pipeline Orchestration:
Design, develop, and maintain complex data pipelines using Astronomer and Airflow.
* Cloud
Infrastructure: Deploy and manage services within Google Cloud Platform, including Big Query, Dataflow, and Cloud Run.
* Infrastructure as Code (IaC):
Use Terraform for environment provisioning and Tekton for CI/CD pipeline automation.
* Data Analysis & Modeling:
Write advanced SQL queries and utilize Python to clean, transform, and analyze large-scale datasets.
* Visualization & Storytelling:
Build intuitive dashboards in Looker Studio to track KPIs and provide visibility to stakeholders.
* Stakeholder Engagement:
Communicate technical findings to non-technical audiences and collaborate with upper management to align data strategy with business goals.
* Software Best Practices:
Utilize Git Hub and VS Code for version control and collaborative development.
* Pipeline Orchestration:
Design, develop, and maintain complex data pipelines using Astronomer and Airflow.
* Cloud
Infrastructure: Deploy and manage services within Google Cloud Platform, including Big Query, Dataflow, and Cloud Run.
* Infrastructure as Code (IaC):
Use Terraform for environment provisioning and Tekton for CI/CD pipeline automation.
* Data Analysis & Modeling:
Write advanced SQL queries and utilize Python to clean, transform, and analyze large-scale datasets.
* Visualization & Storytelling:
Build intuitive dashboards in Looker Studio to track KPIs and provide visibility to stakeholders.
* Stakeholder Engagement:
Communicate technical findings to non-technical audiences and collaborate with upper management to align data strategy with business goals.
* Software Best Practices:
Utilize Git Hub and VS Code for version control and collaborative development.
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