Data Product & Governance Lead

GG BRANDS COMPANY•Sharonville, OH

23h•Onsite

About The Position

The Manager, Data Products & Governance leads the enterprise data product portfolio, the master data management program, and the data governance program that supports how data and artificial intelligence are used across the company. The role has three pillars: data product management, master data management, and data governance and AI enablement. It also leads a team of three data governance and master data specialists, with responsibility for their priorities, development, and day-to-day execution. Together these pillars set the standards, ownership model, and controls that make data trusted, well understood, and appropriately protected; keep master data accurate and consistent across the systems that depend on it; support the practical guardrails that let teams adopt AI capabilities safely and productively rather than informally and invisibly; and prioritize the data product work that turns standards into capabilities people actually use. Beyond the team, the role leads through influence: holding governance forums, developing a federated network of data owners and stewards, and partnering across Information Technology, IT Security, Legal, Human Resources, Marketing, Supply Chain, and Finance to turn written policy into everyday practice. The division of responsibility between this role and the AI Center of Excellence is still being defined; this role brings the data expertise, standards, and controls that AI governance depends on, and partners with the COE on tool, model, and usage policy. Decisions to purchase third-party or syndicated data sets rest with the business functions that hold the business case and the budget. This role defines the standards those data sets must meet, evaluates fit and quality, and advises on selection; it does not decide what is bought.

Requirements

Bachelor's degree in Information Systems, Data Analytics, Computer Science, Business, or a related field, or equivalent practical experience.
7+ years of combined experience across data governance, master data management, enterprise data management, data product management, analytics, or data architecture, including time leading an enterprise-level program or portfolio.
2+ years of direct people leadership, including setting priorities, developing team members, giving candid feedback, and managing performance.
Demonstrated experience defining product strategy, managing a roadmap and backlog, and delivering data or analytics capabilities that produced measurable business outcomes.
Hands-on experience with master data management across domains such as item, customer, or supplier, including domain standards, hierarchies, match and merge rules, request and change workflows, and measurable improvement in record accuracy and turnaround.
Demonstrated experience building or substantially maturing a data governance program, including the operating model, policies, standards, stewardship network, and governance forums.
Direct experience authoring enterprise policy or standards that were adopted and sustained, ideally covering acceptable use, data classification, or AI governance.
Working knowledge of AI and generative AI capabilities and their practical risks, including data leakage, inaccurate or fabricated output, bias, intellectual property and confidentiality exposure, and vendor model dependencies.
Understanding of relevant privacy and data protection requirements, such as GDPR and CCPA/CPRA, and the ability to work with Legal to translate obligations into operational standards.
Strong understanding of data quality, metadata, master and reference data, lineage, and data classification concepts.
Working knowledge of modern data platforms, cloud data environments, and business intelligence tools, and an understanding of how governance controls are actually implemented in them.
Ability to translate between business needs and technical requirements without losing the intended business outcome.
Proven ability to influence and align senior stakeholders across IT, Legal, Security, and business functions without direct authority.
Strong written and verbal communication skills, with the ability to make complex data concepts practical and to write policy and product documentation that is short, usable, and understandable by a non-technical audience.
Sound judgment in balancing risk against speed, and the willingness to say no, say yes with conditions, or escalate, with a documented rationale in each case.

Nice To Haves

Experience in manufacturing, consumer packaged goods, retail, or a multi-functional global enterprise environment is a plus.

Responsibilities

Develop and maintain a prioritized roadmap for enterprise data products, aligned to business strategy and measurable value.
Partner with business leaders and end users to understand the decisions, workflows, pain points, and opportunities that data products should support.
Translate business needs into product requirements, user stories, acceptance criteria, and clear outcomes for data engineering, architecture, analytics, and application teams.
Manage backlog prioritization and make tradeoff recommendations based on business impact, user value, risk, effort, dependencies, and data readiness.
Guide data products through discovery, design, delivery, launch, adoption, enhancement, and retirement, including user validation and release readiness.
Drive adoption through stakeholder communication, change planning, documentation, training coordination, and feedback loops.
Identify opportunities to simplify fragmented reporting and data solutions, increase reuse, and reduce one-off or duplicative work.
Partner with architecture and engineering to embed governance and product requirements into platform design, integration patterns, and semantic models rather than inspecting for them afterward.
Lead the master data management program across domains such as item, customer, and supplier, setting standards for record creation, enrichment, validation, and change so master data stays accurate and consistent across the ERP and the systems that consume it.
Govern master data intake and change workflows, including validation, approval routing, and turnaround expectations, so the business gets accurate records at the speed it needs.
Maintain domain hierarchies, match and merge rules, and record standards so master data supports reporting, planning, and downstream transactions without rework.
Set data quality standards and measurement for master and transactional data; monitor quality against agreed thresholds and drive root-cause resolution with the accountable business and technical owners.
Partner with Supply Chain, Marketing, Finance, and Sales to resolve competing definitions and align master data standards with how each function actually uses the records.
Maintain and evolve the enterprise data governance framework, including the operating model, decision rights, policies, standards, and supporting procedures.
Partner with Legal & IT Security to apply data classification, access, retention, and data-sharing standards across cloud and on-premises platforms, including what may be used with AI tools.
Ensure priority data products carry the ownership, quality, documentation, access, and controls that advanced analytics and AI depend on, so governance and product work reinforce each other rather than compete.
Partner with the AI COE and cross-functional teams (Legal, IT Security, HR) to align data and AI policy with regulatory obligations, contractual commitments, and customer and retailer requirements, including input on data and AI terms in vendor and SaaS agreements.
Apply the data governance exception and escalation process to data and AI requests with documented controls, and monitor industry best practice to advise the AI COE and IT leadership on standards changes as capabilities evolve.
Lead a team of data governance and master data specialists: set priorities, balance workload, remove obstacles, and hold a consistent standard for quality and turnaround.
Coach and develop team members through regular one-on-ones, clear expectations, candid feedback, and individual development plans; participate in hiring as the program grows.
Build the team's capacity to run governance and master data processes independently, so the program does not depend on any single person.
Define and report program metrics such as master data accuracy and turnaround, data quality performance, issue aging, policy exceptions, data use case throughput, and product adoption, and communicate roadmap, progress, risks, and dependencies to technical and business stakeholders.
Represent the team's work, capacity, and constraints to IT and business leadership, and lead through influence beyond the team by challenging one-off requests, surfacing tradeoffs plainly, and building alignment around shared enterprise priorities.
Serve as a hands-on player-coach, directly supporting complex data governance, master data, and data product work while providing practical guidance and quality oversight to team members responsible for day-to-day execution.