Full Stack Data Engineer

In this role, you''ll be responsible for designing, building, and optimizing scalable data pipelines within our Google Cloud Platform (GCP) environment. You''ll work with GCP Native technologies like BigQuery, Dataflow, and Pub/Sub, ensuring data governance, security, and optimal performance. This is a fantastic opportunity to leverage your full-stack expertise, collaborate with talented teams, and establish best practices for data engineering at Ford.

Data Pipeline Builder: Spearhead the design, development, and maintenance of scalable data ingestion and curation pipelines from diverse sources. Ensure data is standardized, high-quality, and optimized for analytical use. Leverage cutting-edge tools and technologies, including Python, SQL, and DBT/Dataform, to build robust and efficient data pipelines.

End-to-End Integration Expert: Utilize your full-stack skills to contribute to seamless end-to-end development, ensuring smooth and reliable data flow from source to insight.

GCP Data Solutions Leader: Leverage your deep expertise in GCP services (BigQuery, Dataflow, Pub/Sub, Cloud Functions, etc.) to build and manage data platforms that not only meet but exceed business needs and expectations.

Data Governance Security Champion: Implement and manage robust data governance policies, access controls, and security best practices, fully utilizing GCP''s native security features to protect sensitive data.

Data Workflow Orchestrator: Employ Astronomer and Terraform for efficient data workflow management and cloud infrastructure provisioning, championing best practices in Infrastructure as Code (IaC).

Performance Optimization Driver: Continuously monitor and improve the performance, scalability, and efficiency of data pipelines and storage solutions, ensuring optimal resource utilization and cost-effectiveness.

Collaborative Innovator: Collaborate effectively with data architects, application architects, service owners, and cross-functional teams to define and promote best practices, design patterns, and frameworks for cloud data engineering.

Automation Reliability Advocate: Proactively automate data platform processes to enhance reliability, improve data quality, minimize manual intervention, and drive operational efficiency.

Effective Communicator: Clearly and transparently communicate complex technical decisions to both technical and non-technical stakeholders, fostering understanding and alignment.

Continuous Learner: Stay ahead of the curve by continuously learning about industry trends and emerging technologies, proactively identifying opportunities to improve our data platform and enhance our capabilities.

Business Impact Translator: Translate complex business requirements into optimized data asset designs and efficient code, ensuring that our data solutions directly contribute to business goals.

Documentation Knowledge Sharer: Develop comprehensive documentation for data engineering processes, promoting knowledge sharing, facilitating collaboration, and ensuring long-term system maintainability.