Data Engineer

Ford Motor Company is a global mobility and automotive company with technology-focused businesses serving commercial and fleet customers. The Full Stack Data Engineer will design, build, and maintain end-to-end data solutions spanning data ingestion, APIs, semantic data layers, governance, and analytics. The role collaborates with analysts, developers, and stakeholders to deliver reliable, documented, secure, and accessible data.

Responsibilities:

• Design, build, and maintain scalable ETL/ELT pipelines to ingest, transform, and deliver data from diverse sources into Google BigQuery and other cloud data platforms
• Develop, document, and maintain RESTful/GraphQL APIs to expose data and services to internal applications, dashboards, and third-party consumers
• Design and manage a semantic data layer (e.g., LookML, dbt semantic models, Cube.js, or similar) that translates raw data into consistent, business-friendly metrics and definitions
• Implement and maintain a data catalog / knowledge catalog (e.g., Google Data Catalog, Collibra, Alation, Atlan) to improve data discoverability, lineage tracking, and metadata management
• Optimize BigQuery performance through partitioning, clustering, query tuning, and cost-management best practices
• Build data quality checks, monitoring, and alerting to ensure pipeline reliability and data trustworthiness
• Collaborate with cross-functional teams (analytics, product, engineering) to gather requirements and translate them into scalable data models and API contracts
• Enforce data governance, security, and access control standards (PII handling, role-based access, encryption)
• Write clean, well-tested, and maintainable code (Python/SQL, and backend frameworks for APIs)
• Participate in code reviews, architecture discussions, and CI/CD pipeline development for data and application deployments
• Document data flows, API specifications, semantic definitions, and system architecture for internal and cross-team use

Requirements:

• Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field or equivalent combination of relevant education and experience
• 5+ years of experience in data engineering, software engineering, or a full-stack data role
• 3+ years in roles demonstrating Strong programming skills in Python, Kotlin, and Java
• 3 years of Strong hands-on experience with Google BigQuery (data modeling, query optimization, cost management, scheduled queries)
• 3+ years of  Proven experience designing and building APIs (REST and/or GraphQL) using frameworks such as FastAPI, Flask, Node.js/Express, or similar
• 3 years of practical experience building and maintaining a semantic layer (e.g., dbt, LookML/Looker, Cube.js, AtScale) to standardize business metrics
• 2+ years of experience in roles requiring a Strong understanding of testing, monitoring, reliability, and maintainable software design
• 2+ years comfortable working years of experience with SQL and structured data
• Candidates for positions with Ford Motor Company must be legally authorized to work in the United States
• Master's degree in Computer Science, Engineering, Information Systems, or a related field
• Experience working with a data catalog / metadata management tool (e.g., Google Data Catalog, Collibra, Alation, Atlan, Amundsen) including lineage, tagging, and governance workflows
• Strong SQL skills and experience with Python for data pipeline development and automation
• Experience with cloud platforms, preferably Google Cloud Platform (GCP) (Cloud Storage, Cloud Functions, Dataflow, Pub/Sub, Composer/Airflow)
• Solid understanding of data modeling concepts (dimensional modeling, star/snowflake schemas, normalization)
• Familiarity with version control (Git) and CI/CD practices for data and application deployments
• Strong understanding of data governance, security, and privacy best practices
• Experience with orchestration tools such as Apache Airflow / Cloud Composer
• Familiarity with dbt (data build tool) for transformation and testing
• Experience with containerization (Docker/Kubernetes) and infrastructure-as-code (Terraform)
• Exposure to streaming data platforms (Kafka, Pub/Sub, Dataflow)
• Experience in the automotive, manufacturing, or connected vehicle data domain (a plus, given Ford's business context)
• Knowledge of BI/visualization tools (Looker, Tableau, Power BI)
• Understanding of data mesh or domain-driven data architecture principles