Manager, Data Modeling & Governance

The J.M. Smucker Co.•Orrville, OH

4d•Hybrid

About The Position

The Manager, Data Modeling & Governance is responsible for leading a team of data architects and governance professionals while establishing enterprise standards for how data is defined, structured, and governed. This role ensures that data is trusted, consistently defined, and aligned to support scalable analytics and reporting across the organization. This role defines and enforces standards for dimensional modeling using star schema principles, including fact and dimension design, conformed metrics, and semantic consistency across domains. It ensures that analytics data models are reusable, performant, and aligned to business needs, while supporting a unified semantic layer that enables reliable reporting and analytics, including Tableau. In addition, this role owns the enterprise data governance framework, including metadata, lineage, data ownership, and classification. Governance practices are designed to be embedded into daily workflows, ensuring data is discoverable, accountable, and usable. This role defines what data means and how it is structured, complementing data engineering and platform engineering capabilities across the enterprise data platform.

Requirements

Bachelor’s Degree or equivalent experience
10+ years of experience in data modeling, governance, or data architecture
3+ years of experience managing and developing technical teams
Proven experience implementing enterprise data governance programs
Strong experience defining and enforcing data modeling standards at scale
Experience supporting enterprise analytics and reporting environments
Deep expertise in dimensional modeling using star schema for analytics and reporting
Strong experience in data governance frameworks and execution
Experience with metadata management, lineage, and data catalogs such as Atlan
Strong understanding of analytics ecosystems, including Tableau
Experience aligning data models with business definitions and reporting requirements

Nice To Haves

Lakehouse and layered data architectures
Data quality frameworks and validation practices
Data access controls and governance implementation approaches
Modern data platform concepts and workflows

Responsibilities

Lead, coach, and develop a team of Data Architects and Data Governance professionals
Set clear priorities, performance expectations, and deliverables
Establish consistent modeling and governance practices across domains
Build team capabilities in dimensional modeling, governance execution, and business alignment
Create and maintain scalable documentation, standards, and training materials
Define and enforce enterprise standards for dimensional modeling using star schema principles
Establish consistent design patterns for fact tables, dimension tables, conformed dimensions, and metric definitions
Ensure models are optimized for analytics, reporting, and usability, including Tableau
Promote reusable, domain-aligned models that reduce redundancy and duplication
Define and maintain consistent business definitions across the enterprise
Establish a unified semantic layer supporting Tableau and certified data sources
Resolve discrepancies in metric definitions and data interpretation
Ensure alignment between source data, engineered data layers, and published analytics assets
Own and evolve the enterprise data governance operating model
Establish standards for data classification, sensitivity, retention, and lifecycle management
Define requirements for metadata completeness, ownership, and stewardship
Track governance adoption, maturity, and compliance across domains
Own metadata strategy and governance tooling, including platforms such as Atlan and Unity Catalog
Drive completeness and accuracy of business definitions, lineage, and ownership
Improve discoverability and usability of enterprise data assets
Increase adoption of catalog and governance workflows
Define how governance and modeling standards are embedded into data engineering workflows
Establish standards for certified datasets and trusted data sources
Define promotion patterns from raw to curated to published data layers
Ensure governance supports efficient delivery and consumption of analytics