Data & AI Governance Lead
BravoTECH
Job Description Our Dallas based manufacturing client is looking for a Data & AI Governance Lead. This is a foundational enterprise role responsible for building and operationalizing our client's Data & AI Governance capability from the ground up.
JOB RESPONSIBILITIES Governance Buildout & Adoption
Governance Program Management
Business Engagement & Change Leadership
Platform, Policy & AI Governance
JOB RESPONSIBILITIES Governance Buildout & Adoption
- Build company's enterprise Data & AI Governance operating model, including governance roles, decision rights, stewardship responsibilities, escalation paths, templates, operating cadence, and adoption measures.
- Establish practical governance routines that fit company's current maturity level while creating durable enterprise standards.
- Identify, onboard, and coach data owners and domain stewards across Finance, Sales, Operations, Procurement, Project Management, Product Engineering, and HR.
- Drive adoption in an environment where business ownership, data discipline, and governance accountability are still maturing.
- Demonstrate governance value through tangible business outcomes such as fewer KPI disputes, reduced manual reconciliation, improved data quality, faster reporting, and stronger trust in enterprise dashboards.
- Create, own, and maintain the enterprise KPI registry, ensuring key metrics have a clear definition, formula, source system, owner, cadence, and approval status.
- Facilitate cross-functional alignment sessions to resolve inconsistent definitions across SBGs and domains.
- Define and operationalize master data standards across Customer, Vendor, Product / Item, Project, Employee / Workforce, and Finance domains.
- Establish data quality monitoring, issue intake, root-cause analysis, remediation tracking, and governance scorecards.
- Serve as the operational triage point for governance escalations and resolve issues at the lowest appropriate level before escalation to senior governance forums.
Governance Program Management
- Chair and facilitate the L3 Data & AI Governance Working Group and coordinate domain governance routines.
- Build and manage the governance activation roadmap, including first-year priorities, milestones, risks, dependencies, and adoption measures.
- Maintain governance calendars, meeting cadences, agendas, RACI documentation, decision logs, and escalation protocols.
- Manage interdependencies between governance activation, ERP migrations, Microsoft Fabric platform delivery, Power BI certification, and master data standardization.
- Report governance progress, risks, and scorecard results to the L2 Governance Committee and as needed, the L1 Executive Council.
Business Engagement & Change Leadership
- Operate as a business-facing partner across Finance, Operations, Sales, Procurement, Project Management, Engineering, HR, and SBG leadership.
- Build credibility in an industrial, engineered-to-order, project-based environment where product, item, BOM, customer, vendor, project, and margin data can vary significantly across business units.
- Convert stakeholder resistance into adoption by connecting governance to specific business pain points, including manual reconciliation, delayed reporting, duplicated master data, inconsistent project margin visibility, and lack of cross-SBG comparability.
- Help business stakeholders distinguish between legitimate business model differences and avoidable data or process inconsistency.
- Build domain steward capability through onboarding, coaching, training, and practical business education.
Platform, Policy & AI Governance
- Partner with Data Engineering, Analytics, ERP, and Platform teams to ensure KPI standards, master data rules, metadata, lineage, and data quality requirements are embedded into Microsoft Fabric, Power BI, and Microsoft Purview.
- Support Power BI dataset certification and reporting standards to prevent data model fragmentation at the consumption layer.
- Author and maintain core Data & AI governance policies, including data classification, data quality, data access, master data, lineage, retention, AI acceptable use, and AI use case intake.
- Define and maintain the AI use case intake, risk classification, approval, and tracking process in partnership with Legal, Security, and business stakeholders.
- Bachelor's degree in Business, Information Systems, Finance, Operations Management, Data Management, or related field, or equivalent practical experience.
- 8+ years of experience in data governance, data management, master data management, data quality, enterprise analytics governance, or related function.
- Demonstrated experience building or materially transforming a data governance capability in a low-maturity or decentralized organization.
- Experience creating governance structure, policies, procedures, stewardship routines, data ownership models, KPI definitions, and adoption mechanisms where they did not previously exist.
- Experience working in environments with fragmented systems, inconsistent business definitions, manual reporting, poor data quality, unclear ownership, and business resistance to standardization.
- Experience operating in acquisition-driven, multi-entity, multi-ERP, or decentralized business environments where business units have different processes, systems, cultures, and maturity levels.
- Experience designing or implementing KPI registries, business glossaries, data dictionaries, data quality processes, stewardship models, master data standards, or governance operating models.
- Experience partnering with data platform, data engineering, BI, ERP, or analytics teams to translate governance standards into technical implementation.
- Strong facilitation, program management, stakeholder engagement, conflict resolution, and change leadership skills.
- Experience in manufacturing, industrial products, engineered solutions, distribution, project-based businesses, or similar operationally complex environments preferred.
- Experience with customer, vendor, product/item, BOM, project, procurement, operations, finance, and employee/workforce data domains preferred.
- Familiarity with business KPIs such as revenue recognition, gross margin, EBITDA, project margin, bookings, backlog, on-time delivery, procurement spend variance, supplier performance, SQDC, and working capital preferred.
- Hands-on experience with Microsoft Purview, Collibra, Alation, or similar data governance platforms preferred.
- Familiarity with Microsoft Fabric, Power BI, D365 F&O, Epicor, TM1, or comparable ERP/planning/reporting ecosystems preferred.
- Experience with AI governance, responsible AI policies, AI use case intake, or AI risk classification preferred.
- Experience in mature regulated industries such as banking, financial services, or insurance is welcome if paired with direct ownership of governance buildout, transformation, or adoption in a low-maturity environment preferred.
- Preferred but not required:
- PMP or equivalent program management certification
- Prosci or equivalent change management certification
- DAMA CDMP or equivalent data management certification
- Data Governance & Stewardship certification
- Microsoft Purview, Power BI, Fabric, or Microsoft Data / AI certification
- Demonstrated experience building governance capability in low-maturity environments is more important than certifications alone.
Vacancy posted 3 days ago
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