Manager – Data Product Management

Role Overview:

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 3^rd 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.

Qualifications:

• Bachelor’s degree in Computer Science, Engineering, Data Science, Information Systems, Statistics, Applied Mathematics, Business Analytics, or a related field; an advanced degree is a plus.
• 8+ years of experience in data management, data engineering, analytics, or product management, with at least 3 years focused on product management, and a track record of delivering enterprise-scale data solutions.
• 3+ years of people leadership experience, including managing managers/supervisors or leading large, multi-team organizations.
• Strong understanding of data concepts, including data modeling, data architecture, data lakes/lakehouses, master and reference data, metadata, and lineage; hands-on familiarity with cloud data environments (GCP, AWS, or Azure).
• Experience with data governance, data quality, and data privacy and security in an enterprise environment.
• Experience defining and delivering data products or data-as-a-product approaches, including data contracts, service levels, and product metrics.
• Proven ability to define and execute product roadmaps and to manage complex, cross-functional programs in a fast-paced environment.

• Excellent communication and stakeholder management skills, with the ability to influence senior leaders and articulate technical concepts in business language.

Preferred

• experience in the automotive or manufacturing industry; familiarity with semantic layers, business glossaries, and data catalog tools; exposure to AI/ML and generative AI data requirements; and experience with data visualization and analytics tools.
• Experience within the Google Cloud Platform
• Experience with Agile/Scrum methodologies, including use of Jira, is a plus

Relocation assistance is available for this role.

Sponsorship is available for this role.

• Immediate medical, dental, vision and prescription drug coverage

• Flexible family care days, paid parental leave, new parent ramp-up programs, subsidized back-up child care and more

•  Family building benefits including adoption and surrogacy expense reimbursement, fertility treatments, and more

• Vehicle discount program for employees and family members and management leases

• Tuition assistance

• Established and active employee resource groups

• Paid time off for individual and team community service 

• A generous schedule of paid holidays, including the week between Christmas and New Year’s Day 

• Paid time off and the option to purchase additional vacation time. 

1. Data Product Strategy & Vision:

• 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.

2. Domain Data Hub Leadership & People Management:

• 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.

3. Business Concepts & Data Product Development:

• 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 operate AI/ML and generative AI solutions.

4. Data Quality, Governance & Trust:

• Own data quality for the domain data hubs, defining quality rules, measures, and thresholds, monitoring them, and driving remediation of issues at their source.
• Partner with Data Governance, Privacy, and Security teams to ensure data is classified, protected, and accessed in line with regulatory and compliance requirements (e.g., GDPR, CCPA, and other applicable data privacy regulations) and Ford policy.
• Establish clear data ownership and stewardship for every business concept and data product, including metadata, lineage, and catalog documentation to make data discoverable and understandable.
• Define and manage data lifecycle policies, including retention, archival, and decommissioning of data.

5. Stakeholder Management & Customer Engagement:

• Collaborate with key stakeholders across business units (e.g., Engineering, Manufacturing, Sales & Marketing, Finance, Analytics, IT, Security, Compliance) to understand their data needs and translate them into data products and business concepts.
• Conduct ongoing customer discovery through user interviews, surveys, and regular pulse checks to understand pain points, measure satisfaction, and identify emerging needs.
• Manage relationships with senior leadership, providing regular product reviews on progress, outcomes, risks, and opportunities.
• Act as a liaison between business, data producers, data consumers, and platform teams, ensuring clear communication and alignment of goals.

6. Data Platform Partnership:

• Partner with the Enterprise Data Platform Product Manager and platform teams to communicate data-driven requirements and ensure platform capabilities (e.g., ingestion, catalog, access control, compute) support the needs of the data products; the platform itself is owned by others.
• Provide input to the platform roadmap based on the needs of domain hubs and data consumers.

7. Adoption, Enablement & Cost Stewardship:

• Drive adoption and reuse of data products through “marketing”, education, and evangelism.  Including onboarding, training, documentation, and best-practice guides, and by promoting data products through the enterprise catalog. 
• Gather and analyze usage data and user feedback to continuously improve data product quality, usability, and value.
• Manage the data portfolio budget, working with finance and procurement to optimize data storage, duplication, and consumption costs while delivering high-value data products.

8. Continuous Improvement & Innovation:

• Stay informed about trends in data product management, data mesh and fabric hybrid models, and data-as-a-product approaches, semantic modeling, data governance, and AI to drive innovation.
• Foster a culture of continuous improvement, experimentation, and data literacy within the team and across the enterprise