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.
Model Quality & Standards
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.
Semantic Alignment & Analytics Support
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.
Data Architecture & Solution Design
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.
Governance & Documentation
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.
What we are looking for
Minimum 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
Additional skills and experience that we think would make someone successful in this role (not required):
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
The Right Place for You
We are bold, kind, strive to do the right thing, we play to win, and we believe in a strong community that thrives together. Our culture is rooted in our Basic Beliefs, and we believe in supporting every employee by meeting their physical, emotional, and financial needs.
Stay connected with us on LinkedIn®
We're an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, genetic information, age, national origin, disability status or protected veteran status.
