Basic Qualifications:

Bachelor’s or Master’s degree in a Computer Science, Engineering or a related or related field of study
5+ Years - Ability to work effectively across organizations, product teams and business partners.
5+ Years - Knowledge Agile (Scrum) Methodology, experience in writing user stories
5+ Years - Strong understating of Database concepts and experience with multiple database technologies – optimizing query and data processing performance.
5+ Years - Full Stack Data Engineering Competency in a public cloud – Google, MS Azure, AWS
Critical thinking skills to propose data solutions, test, and make them a reality.
5+ Years - Highly Proficient in SQL, Python, Java, Scala, or Go (or similar) - Experience programming engineering transformation in Python or a similar language.
5+ Years Demonstrated ability to lead data engineering projects, design sessions and deliverables to successful completion.
Cloud native technologist
Deep understanding of data service ecosystems including data warehousing, lakes, metadata, meshes, fabrics and AI/ML use cases.
User experience advocacy through empathetic stakeholder relationship.
Effective Communication both internally (with team members) and externally (with stakeholders)
Knowledge of Data Warehouse concepts – experience with Data Warehouse/ ETL processes
Strong process discipline and thorough understating of IT processes (ISP, Data Security).

Responsibilities

Responsibilities:

Interact with GDIA product lines and business partners to understand data engineering opportunities, tooling and needs.
Collaborate with Data Engineering and Data Architecture to design and build templates, pipelines and data products including automation, transformation and curation using best practices
Develop custom cloud solutions and pipelines with GCP native tools – Data Prep, Data Fusion, Data Flow, DBT and Big Query
Operationalize and automate data best practices: quality, auditable, timeliness and complete
Participate in design reviews to accelerate the business and ensure scalability
Work with Data Engineering and Architecture and Data Platform Engineering to implement strategic solutions
Advise and direct team members and business partners on Ford standards and processes.

Qualifications

Preferred Qualifications:

Excellent communication, collaboration and influence skills; ability to energize a team.
Knowledge of data, software and architecture operations, data engineering and data management standards, governance and quality
Hands on experience in Python using libraries like NumPy, Pandas, etc.
Extensive knowledge and understanding of GCP offerings, bundled services, especially those associated with data operations Cloud Console, BigQuery, DataFlow, DataFusion, PubSub / Kafka, Looker Studio, VertexAI
Experience with Teradata, Hadoop, Hive, Spark and other parts of legacy data platform
Experience with recoding, re-developing and optimizing data operations, data science and analytical workflows and products.
Data Governance concepts including GDPR (General Data Protection Regulation), CCPA (California Consumer Protection Act), PoLP and how these can impact technical architecture