Manager, Data Product Management

As the Manager, Data Product Management for the Enterprise Data Platform, you will own the strategy, quality, and business value of the data held in Ford’s Enterprise Data Lake – the single home for company line of business data, and 3rd Party Data, which has been architected, modelled, and curated to serve Analytics, AI, Reporting, and Operational Data Store (ODS) use cases.

Your focus is the data itself. You will ensure the data is trusted, discoverable, well-governed, and expressed in the language of the business. You will lead a team of 6 Supervisors, each accountable for defining, designing, building, and managing a “Domain Data Hub.” Each hub delivers business concepts – curated collections of data products that describe a business concept (e.g., Customer, Vehicle, Dealer, Part, Order) in business terms.

This role requires strong data product management expertise, an understanding of data modeling and governance, people leadership, and the ability to engage senior stakeholders across the enterprise.

• Develop and articulate the data product vision for the Enterprise Data environment for Analytics and AI and its domain data hubs, aligning it with Ford’s broader data, AI, and digital transformation goals.
• Define and drive the data product roadmap, prioritizing domains, business concepts, and data products based on business value, customer feedback, data-driven insights, and enterprise priorities. This will include partnering with Democratized (non-direct reporting relationship) data domain Data Hub owners.
• Establish and manage the enterprise data product framework, including standards for what constitutes a data product, a business concept, and a domain data hub, and how they relate to one another.
• Define and track value and outcomes for the data portfolio, including adoption, reuse, time-to-data, business impact, and retirement of redundant or low-value data assets.

• Lead, coach, and develop Supervisors, each responsible for the end-to-end lifecycle (define, design, build, and manage) of a domain data hub; set clear objectives, accountabilities, and performance expectations.
• Ensure consistent practices across hubs, including common design patterns, naming and modeling conventions, definition-of-done, and release and support processes, while allowing domains to reflect their unique business needs.
• Manage cross-hub dependencies and shared concepts, resolving overlaps, conflicting definitions, and duplicate data to deliver a single, consistent view of enterprise concepts.
• Allocate resources and manage the portfolio across hubs, balancing capacity, skills, and priorities, and building a strong talent pipeline and culture of data product ownership.

• Oversee the design and delivery of business concepts that describe the business in business language, supported by a shared business glossary, semantic definitions, and consistent metrics.
• Ensure each data product is purpose-built and fit for use for Analytics, AI/ML, Reporting, and ODS consumers, with clearly defined owners, schemas, service levels, and data contracts.
• Partner with source-system owners and data producers to ensure the right data is sourced, well understood, and delivered to the lake with agreed quality and timeliness expectations.
• Partner with Data Architecture on data modeling and integration standards for structured, unstructured, batch, and real-time data, including master and reference data alignment and lineage from source to consumption.
• Ensure data readiness for AI by delivering high-quality, well-documented, appropriately governed data products that can be used to train, ground, and…