Full Stack Data Engineer

We are the movers of the world and the makers of the future. We get up every day, roll up our sleeves, and build a better world - together. At Ford, we're all a part of something bigger than ourselves. Are you ready to change the way the world moves?

Do you believe data is the engine driving the future of mobility? We do! Transforming how Ford manages, analyzes, and leverages financial data requires scalable data platforms, reliable cloud infrastructure, and high-quality analytical products that enable timely, data-driven decision-making. That's where the Finance Data Hub makes an impact. We are modernizing how Ford manages financial data globally, delivering trusted and secure data products that support critical finance initiatives across the enterprise.

We are seeking a talented and driven Full Stack Data Engineer to join our product team. In this role, you will build scalable, high-performance data pipelines and cloud infrastructure that power financial reporting, analytics, and strategic decision-making. You should have a strong technical background and demonstrate experience in Google Cloud Platform (GCP), data warehousing, batch and streaming pipeline development, infrastructure automation, and modern software engineering practices.

Responsibilities include the end-to-end design, development, deployment, optimization, and production support of finance data products-from ingestion and transformation through governance, quality monitoring, and delivery. Working in an Agile, customer-centric environment and in close partnership with analytics stakeholders, product managers, and cross-functional engineers, you will deliver secure, reliable, cost-effective, and high-performing data solutions at enterprise scale.

We are seeking a talented and driven Full Stack Data Engineer to join our product team. In this role, you will build scalable, high-performance data pipelines and cloud infrastructure that power financial reporting, analytics, and strategic decision-making. You should have a strong technical background and demonstrate experience in Google Cloud Platform (GCP), data warehousing, batch and streaming pipeline development, infrastructure automation, and modern software engineering practices. Responsibilities include the end-to-end design, development, deployment, optimization, and production support of finance data products-from ingestion and transformation through governance, quality monitoring, and delivery. Working in an Agile, customer-centric environment and in close partnership with analytics stakeholders, product managers, and cross-functional engineers, you will deliver secure, reliable, cost-effective, and high-performing data solutions at enterprise scale.

• Pipeline Development & Ingestion: Design, build, and scale robust batch and streaming data pipelines on Google Cloud Platform (GCP) to process large volumes of finance data.
• Data Warehousing & Architecture: Develop exceptional analytical data products applying solid data warehouse principles, data modeling, and best practices.
• Infrastructure & DevOps: Maintain and enhance the platform's infrastructure using Terraform (Infrastructure as Code) and continuously develop, evaluate, and deploy code using CI/CD pipelines.
• Stakeholder Collaboration: Partner closely with data analytics stakeholders to streamline and optimize data acquisition, processing, and presentation workflows.
• Data Governance & Quality: Implement and promote enterprise data governance models focusing on data protection, sharing, reuse, standards, quality monitoring, and data lineage documentation.
• Code Quality & Security: Write clean, reliable code using Test-Driven Development (TDD) in an agile environment, actively addressing security vulnerabilities and code quality issues using tools like SonarQube, Checkmarx, Fossa, and Cycode.
• Optimization & Cost Management: Continuously optimize existing data solutions (pipelines, infrastructure, and products) to ensure high performance, security, reliability, low vulnerability, and cost efficiency.
• Production Support: Monitor production pipelines and provide timely production support to resolve issues in accordance with established SLAs.
• Continuous Improvement: Stay current on modern data engineering practices, contribute to the company's technical direction, and proactively build domain expertise in finance data.
• Design, build, and scale robust batch and streaming data pipelines on Google Cloud Platform (GCP) to process large volumes of finance data
• Pipeline Development & Ingestion: Design, build, and scale robust batch and streaming data pipelines on Google Cloud Platform (GCP) to process large volumes of finance data.
• Data Warehousing & Architecture: Develop exceptional analytical data products applying solid data warehouse principles, data modeling, and best practices.
• Infrastructure & DevOps: Maintain and enhance the platform's infrastructure using Terraform (Infrastructure as Code) and continuously develop, evaluate, and deploy code using CI/CD pipelines.
• Stakeholder Collaboration: Partner closely with data analytics stakeholders to streamline and optimize data acquisition, processing, and presentation workflows.
• Data Governance & Quality: Implement and promote enterprise data governance models focusing on data protection, sharing, reuse, standards, quality monitoring, and data lineage documentation.
• Code Quality & Security: Write clean, reliable code using Test-Driven Development (TDD) in an agile environment, actively addressing security vulnerabilities and code quality issues using tools like SonarQube, Checkmarx, Fossa, and Cycode.
• Optimization & Cost Management: Continuously optimize existing data solutions (pipelines, infrastructure, and products) to ensure high performance, security, reliability, low vulnerability, and cost efficiency.
• Production Support: Monitor production pipelines and provide timely production support to resolve issues in accordance with established SLAs.
• Continuous Improvement: Stay current on modern data engineering practices, contribute to the company's technical direction, and proactively build domain expertise in finance data.
• Design, build, and scale robust batch and streaming data pipelines on Google Cloud Platform (GCP) to process large volumes of finance data