Software Engineer, Platform Systems (SDV/FNV)

Ford Motor Company; Dearborn (MI); Hybrid; USD 120,000 - 160,000; Full time

Benefits offered by this job

Hybrid work model

Job summary

Ford Motor Company in Dearborn, Michigan, seeks a Software Engineer for Platform Systems to design and build scalable data pipelines and microservices on GCP. This hybrid role requires living within commuting distance to Dearborn and contributing to real-time and batch analytics.

Responsibilities include data ingestion, governance, and performance optimization, with strong collaboration across cross-functional teams to ensure robust data platforms and secure access controls.

Qualifications

• Experience designing, building and optimizing scalable data pipelines on cloud platforms.
• Proficient in GCP services and modern data engineering patterns.
• Ability to design SOA/microservices based data solutions with security in mind.

Responsibilities

• Architect and maintain scalable data pipelines and microservices.
• Lead data ingestion and integration from diverse sources.
• Collaborate with data architects and engineers to enforce best practices.
• Automate platform processes to enhance reliability and efficiency.

Skills

Data pipelines

GCP

SOA

Microservices

Full-stack

Data ingestion

Data governance

Security

Performance optimization

Collaboration

Automation

Tools

BigQuery

Dataflow

Pub/Sub

Cloud Functions

Job description

Software Engineer, Platform Systems (SDV/FNV) - positions offered by Ford Motor Company (Dearborn, Michigan). Note, this is a hybrid position whereby the employee will work both from home and from the aforementioned worksite. Hence, the employee must live within a reasonable commuting distance from the worksite. Design and Build Data Pipelines: Architect, develop, and maintain scalable data pipelines and microservices that support real-time and batch processing on GCP. Service-Oriented Architecture (SOA) and Microservices: Design and implement SOA and microservices-based architectures to ensure modular, flexible, and maintainable data solutions. Full-Stack Integration: Leverage your full-stack expertise to contribute to the seamless integration of front-end and back-end components, ensuring robust data access and UI-driven data exploration. Data Ingestion and Integration: Lead the ingestion and integration of data from various sources into the data platform, ensuring data is standardized and optimized for analytics. GCP Data Solutions: Utilize GCP services (BigQuery, Dataflow, Pub/Sub, Cloud Functions, etc.) to build and manage data platforms that meet business needs. Data Governance and Security: Implement and manage data governance, access controls, and security best practices while leveraging GCPs native row- and column-level security features. Performance Optimization: Continuously monitor and improve the performance, scalability, and efficiency of data pipelines and storage solutions. Collaboration and Best Practices: Work closely with data architects, software engineers, and cross-functional teams to define best practices, design patterns, and frameworks for cloud data engineering. Automation and Reliability: Automate data platform processes to enhance reliability, reduce manual intervention, and improve operational efficiency.

EOE.