Senior Data Scientist / ML Engineer

Data Mesh Strategy & Architecture

• Design and evolve Ford's data mesh architecture across manufacturing and enterprise domains - defining domain boundaries, data product contracts, and interoperability standards.

• Establish and maintain federated computational governance policies (schema standards, data quality SLAs, security classifications, lineage and retention requirements) applied consistently across decentralized domain teams.

• Champion adoption of next-generation technologies and cloud-native patterns (GCP, Vertex AI, agentic AI/LLM orchestration) to modernize legacy data workflows and drive measurable business impact.

Ford Motor Company is seeking an experienced Data Manager to architect and scale data mesh within the Manufacturing domain - leading the shift from centralized, plant-by-plant data ownership to a domain-oriented, self-serve data platform where raw data landing in Cloud Platform from plant floor systems (MES, SCADA, PLC, IoT sensors), quality systems, and traceability platforms is transformed into trusted, discoverable, and independently governed data products. This role sits at the intersection of distributed data architecture, engineering leadership, and federated governance - responsible for defining sub-domain boundaries across manufacturing functions, establishing data-as-a-product standards specific to manufacturing data, and building the self-serve infrastructure that empowers plant and manufacturing-function teams to publish and consume trusted data products without routing through a centralized manufacturing data team.

Data Product Development

• Lead the transformation of raw data landing in cloud platforms from Manufacturing systems, into curated, documented, high-quality data products.
• Define and enforce data product standards: ownership, schema versioning, SLAs for freshness/completeness/accuracy, discoverability, and access controls.
• Oversee the build-out of ingestion, transformation, and publishing pipelines (batch and streaming) that convert raw operational data into standardized, reusable data assets.
• Design and build data structures and semantic layers (ontologies, knowledge graphs) that enable AI agents to interpret enterprise data consistently and with minimal human intervention.

Data Modeling & Architecture

• Design and develop data models (enterprise, conceptual, logical, and physical) for assigned domains; manage and maintain models in a shared repository.
• Partner with product and analytics teams to translate use cases into data requirements, providing architecture guidance, data models, and design reviews.
• Establish and enforce data architecture principles, standards, and best practices across the team's data products (catalog, lineage, observability, security, interoperability).
• Drive data quality initiatives: profile source-system data, define data quality requirements and metrics, and guide monitoring, measurement, reporting, and remediation.

Platform & Tooling

• Drive adoption of cloud services like GCP as the technical backbone of the data mesh and data product ecosystem.
• Evaluate and implement metadata management and data catalog solutions to support discoverability and trust across the mesh.
• Establish monitoring, observability, and SLA-tracking for published data products.

Governance & Compliance

• Own and evolve the federated governance framework, balancing global standards with domain-level autonomy, ensuring compliance with Ford's data security, privacy, and regulatory requirements.
• Implement automated policy enforcement (schema validation, access control, metadata cataloging, lineage tracking) via GCP-native and third-party tooling.
• Partner with cybersecurity, legal, and compliance teams to classify and protect sensitive manufacturing and enterprise data.
• Ensure development patterns support Ford's data security requirements and global privacy regulations (privacy and compliance by design).

Data Product Development

• Lead the transformation of raw data landing in cloud platforms from Manufacturing systems, into curated, documented, high-quality data products.
• Define and enforce data product standards: ownership, schema versioning, SLAs for freshness/completeness/accuracy, discoverability, and access controls.
• Oversee the build-out of ingestion, transformation, and publishing pipelines (batch and streaming) that convert raw operational data into standardized, reusable data assets.
• Design and build data structures and semantic layers (ontologies, knowledge graphs) that enable AI agents to interpret enterprise data consistently and with minimal human intervention.

Data Modeling & Architecture

• Design and develop data models (enterprise, conceptual, logical, and physical) for assigned domains; manage and maintain models in a shared repository.
• Partner with product and analytics teams to translate use cases into data requirements, providing architecture guidance, data models, and design reviews.
• Establish and enforce data architecture principles, standards, and best practices across the team's data products (catalog, lineage, observability, security, interoperability).
• Drive data quality initiatives: profile source-system data, define data quality requirements and metrics, and guide monitoring, measurement, reporting, and remediation.

Platform & Tooling

• Drive adoption of cloud services like GCP as the technical backbone of the data mesh and data product ecosystem.
• Evaluate and implement metadata management and data catalog solutions to support discoverability and trust across the mesh.
• Establish monitoring, observability, and SLA-tracking for published data products.

Governance & Compliance

• Own and evolve the federated governance framework, balancing global standards with domain-level autonomy, ensuring compliance with Ford's data security, privacy, and regulatory requirements.
• Implement automated policy enforcement (schema validation, access control, metadata cataloging, lineage tracking) via GCP-native and third-party tooling.
• Partner with cybersecurity, legal, and compliance teams to classify and protect sensitive manufacturing and enterprise data.
• Ensure development patterns support Ford's data security requirements and global privacy regulations (privacy and compliance by design).