Data Architect, Enterprise Data Platform

J.M. Smucker Co•Akron, OH

9h•Hybrid

About The Position

The Data Architect is responsible for designing and maintaining enterprise data models that support analytics, reporting, and data-driven decision making. This role ensures that data structures are scalable, consistent, and aligned to business needs, while supporting efficient data consumption across the organization. The Data Architect contributes to enterprise analytics data governance and modeling standards by defining data structures, validating implementations, improving model quality and consistency, and working across data domains to ensure alignment between source data, engineered data layers, and published analytics assets. This role applies and enforces dimensional modeling best practices using star schema design, including fact and dimension tables, conformed dimensions, and standardized metrics. Data models are designed to support a unified semantic layer and enable accurate, consistent reporting across tools such as Tableau.

Requirements

Bachelor’s degree, equivalent experience or specialized training in Information Technology
8+ years of experience in data modeling, data architecture, analytics, or senior data engineering environments
Demonstrated ability to collaborate effectively across technical teams, business stakeholders, and data domain partners to drive alignment and adoption
Advanced SQL skills and experience working with large datasets
Experience designing data models, metadata structures, and semantic foundations that support trusted analytics, reporting, and emerging AI use cases
Experience with Databricks, lakehouse architectures, or similar modern cloud data platforms
Strong understanding of data structures, relationships, and performance optimization
Ability to think critically and conceptually, communicate complex data architecture topics clearly, and adapt recommendations for both technical and nontechnical audiences

Nice To Haves

Experience creating and maintaining conceptual, logical, and physical data models using enterprise modeling tools such as ER/Studio, Erwin, or equivalent platforms
Experience leveraging metadata management, data catalog, lineage, and governance capabilities to improve data discoverability, traceability, and trust across enterprise analytics environments, including platforms such as Atlan or similar solutions
Experience working across multiple areas of the analytics lifecycle, including data engineering, data modeling, and business intelligence/reporting solutions
Familiarity with Python and modern data engineering workflows
Familiarity with source control and collaborative development practices (e.g., Git, GitHub, Azure DevOps)
Understanding of modern data platform concepts and workflows
Understanding of how data architecture, metadata, and governance enable trusted analytics and AI solutions

Responsibilities

Design, develop, and maintain dimensional data models using star schema methodology, including defining fact tables, dimension tables, grain, and relationships that support enterprise analytics and reporting.
Ensure models are optimized for performance, scalability, and usability.
Create and maintain conceptual, logical, and physical data models using appropriate modeling tools.
Analyze and profile new data sources to understand structure, quality, relationships, and business context, informing appropriate modeling and architecture decisions.
Apply enterprise data modeling standards and best practices across all solutions.
Validate data models for consistency, accuracy, and alignment with business requirements.
Identify and resolve issues related to duplication, inconsistency, poor model design, or data quality concerns that impact analytics and reporting.
Improve data model usability and clarity for downstream analytics and reporting.
Represent the Enterprise Data Platform team in architecture review boards and design reviews, providing guidance on data modeling, semantic consistency, and analytics architecture considerations.
Establish and maintain a consistent semantic layer, including standardized metrics, dimensions, and business logic that support trusted analytics and reporting.
Align data models with reporting requirements, certified data sources, and enterprise analytics standards.
Support analytics teams by providing clear, well-structured, and consumable data models.
Develop and guide data architecture decisions related to data structures and design patterns.
Collaborate with Data Engineers to ensure data pipeline implementations align with the intent of approved data models, enterprise standards, and architectural best practices while meeting performance and scalability requirements.
Partner with Data Owners, domain experts, engineers, and analytics teams to align data models with business processes, priorities, and enterprise standards.
Recommend improvements to data design, storage, and structure.
Ensure alignment across source, transformed, and published data layers.
Support metadata, lineage, and documentation standards.
Define and document data models, including structure, definitions, and usage guidance.
Ensure models align with governance standards for ownership, classification, and compliance.
Contribute to improving discoverability and trust in enterprise data.
Collaborate with platform, governance, and security teams to ensure data models and architecture designs align with enterprise security, privacy, data classification, and access control standards.