Gdia Data Engineer Jobs

Data Factory Engineering and Operations (DFEO) is GDIA’s organization for making data available to the enterprise at scale and delivering on Ford’s plan to become a data first company. Leveraging our long-term relationship with Google, Ford’s Data Factory can provide near unlimited and unparalleled scale by leveraging the public cloud to capture, ingest, transform, curate, and publish data products for stakeholders. The Data Factory provides centralized access, governance, and discoverability to data consumers looking to build analytical tools, develop new AI/ML models, create data dependent software offerings, and turn data into actionable insights and customer value. Ford’s data first plan, including the Data Factory, remains a strategic priority for leadership and the company.

The Cloud Center of Excellence is looking for a Data Engineer focused on problem solving to accelerate the adoption of the cloud first migration and optimization of the Google Cloud Platform. The successful candidate will work with multiple technical and non-technical teams to develop, document, and evangelize data engineering best practices: define and create patterns to increase usability, acceptance, and Google Cloud Platform enablement. This individual will exhibit deep technical knowledge, leverage agile principles, and have a track record of developing full stack data engineering solutions to support customer needs and objectives: ingestion, transformation, curation, and visualization.

The Cloud Center of Excellence will support the company and the Data Factory investment, development, and product roadmap. The Data Engineer will collaborate with both business and technical teams to define data engineering best practices, innovation, improvement, standards, and templates. The ideal Data Engineer will be a hands-on keyboard full stack data expert willing to challenge status quo, accelerate on-premises migration to the cloud, while becoming a technical expert within the Google Cloud Platform. This individual must be able to take on hands-on tasks such as developing and implementing automated, auditable best practices and processes to support both business and technical communities. Experimentation, ambiguity, and failure is expected; fail fast and fail often. The relentless migration to the cloud.

Required Skills

• Cloud native technologist (ideally GCP)
• Highly Proficient in SQL
• Deep understanding of data service ecosystems including data warehousing, lakes, metadata, meshes, fabrics and AI/ML use cases.
• Python, Java, Scala, or Go (or similar)
• Full Stack Data Engineering Competency in a public cloud – Google, MS Azure, AWS
• Critical thinking skills to propose solutions, test, and make them a reality.
• Effective Communication both internally (with team members) and externally (with stakeholders)
• User experience advocacy through empathetic stakeholder relationship.

Desired Skills

• Data Governance concepts including GDPR (General Data Protection Regulation), CCPA (California Consumer Protection Act), PoLP and how these can impact technical architecture
• Experience with Teradata, Hadoop, Hive, Spark, and other parts of Ford’s legacy data platform to support GCP migration.
• Work as an individual contributor as well as part of a team building, maintaining, and troubleshooting data pipelines.
• Experience with recoding, re-developing, and optimizing data operations, data science and analytical workflows and products.
• Extensive knowledge and understanding of GCP offerings, bundled services, especially those associated with data operations CloudConsole, BigQuery, DataFlow, DataFusion, PubSub / Kafka, Looker Studio, VertexAI

What you’ll receive in return :

As part of the Ford family, you’ll enjoy excellent compensation and a comprehensive benefits package that includes generous PTO, retirement, savings, and stock investment plans, incentive compensation, and much more. You’ll also experience exciting opportunities for professional and personal growth and recognition.

Candidates for positions with Ford Motor Company must be legally authorized to work in the United States. Verification of employment eligibility will be required at the time of hire. Visa sponsorship is not available for this position .

We are an Equal Opportunity Employer committed to a culturally diverse workforce. All qualified applicants will receive consideration for employment without regard to race, religion, color, age, sex, national origin, sexual orientation, gender identity, disability status, or protected veteran status.

For information on Ford's salary and benefits, please visit:

https://corporate.ford.com/content/dam/corporate/us/en-us/documents/careers/2022-benefits-and-comp-GSR-sal-plan-2.pdf

At Ford, the health and safety of our employees is our top priority. Vaccination has been proven to play a critical role in combating COVID-19. As a result, Ford has made the decision to require U.S. salaried employees to be fully vaccinated against COVID-19, unless employees require an accommodation for religious or medical reasons. Being fully vaccinated means that an individual is at least two weeks past their final dose of an authorized COVID-19 vaccine regimen. As a condition of employment, newly hired employees will be required to provide proof of their COVID-19 vaccination or an approved medical or religious exemption.

Position Responsibilities

• Work with other departments such as the Data Factory Innovation Team and Architecture to actively participate in data engineering capabilities and proofs of concept to expand understanding, find technical limitations, explore options, and overcome challenges.
• Work as an individual contributor as well as part of a team building, maintaining, and troubleshooting data pipelines.
• Define and complete full stack data engineering tasks: development, implementation, automation, and monitoring.
• Create high quality, elegant data engineering best practices that focus on cloud-first, encapsulation, repeatability, automation, and auditability.
• Drive delivery of efficient data engineering standards, templates, patterns, and best practices by effectively using agile process methodology to define work, estimate effort, and measure results.