Data Modeler Jobs in Oregon (NOW HIRING)

Certainly. Here's a polished and hiring-manager-ready Data Modeler Job Description that would fit a modern Data & Analytics team supporting Data Engineering, Governance, Analytics, and Product organizations.

About the Role

We are looking for a highly skilled Data Modeler to design, develop, and maintain enterprise-wide data models that support reporting, analytics, operational systems, data products, and AI-driven initiatives.

The ideal candidate will work closely with Data Engineering, Product Management, Data Governance, Analytics, Architecture, and Business teams to ensure data is structured, scalable, governed, and aligned with business objectives. This role is critical in building trusted, reusable, and high-quality data assets across the organization.

Key Responsibilities

Data Modeling & Design

• Design and maintain conceptual, logical, and physical data models for enterprise data platforms.
• Develop scalable data structures to support analytics, reporting, operational applications, and data products.
• Define entities, attributes, relationships, hierarchies, and business rules.
• Create dimensional, relational, and canonical data models based on business requirements.
• Ensure data models support current and future business needs while maintaining consistency across domains.

Data Architecture & Engineering Collaboration

• Partner with Data Engineering teams to implement data models across data lakes, warehouses, and lakehouse architectures.
• Support design and optimization of enterprise data platforms and data pipelines.
• Translate business requirements into technical data structures and schemas.
• Collaborate with solution architects to ensure alignment with enterprise architecture standards.
• Drive adoption of best practices in data modeling and database design.

Product & Analytics Partnership

• Work closely with Product Managers to understand business requirements and define data structures that support product capabilities.
• Collaborate with Analytics teams to enable reporting, dashboards, KPIs, and self-service analytics.
• Support the design of reusable and governed data products that can be leveraged across multiple business functions.
• Participate in discovery sessions and solution design workshops with business stakeholders.

Data Governance & Quality

• Partner with Data Governance teams to establish and enforce data standards, naming conventions, and modeling guidelines.
• Support data lineage, metadata management, and master data initiatives.
• Ensure compliance with data privacy, security, and regulatory requirements.
• Proactively identify and address data quality and consistency issues.
• Promote data stewardship and governance practices across the organization.

Documentation & Standards

• Maintain comprehensive documentation for data models, definitions, and business rules.
• Create and manage data dictionaries, metadata repositories, and lineage documentation.
• Conduct model reviews and recommend improvements.
• Establish and maintain enterprise data modeling standards and best practices.

Stakeholder Management

• Act as a trusted advisor on data structures and information architecture.
• Collaborate with Engineering, Product, Project Management, Governance, and Analytics teams to deliver enterprise data solutions.
• Communicate data design decisions and recommendations to technical and non-technical audiences.
• Support project teams in assessing the impact of business and system changes on data assets.

Required Qualifications

• Bachelor's degree in Computer Science, Information Systems, Engineering, Mathematics, Data Science, or related field.
• 5+ years of experience in Data Modeling, Data Architecture, or Data Warehousing roles.
• Strong expertise in conceptual, logical, and physical data modeling techniques.
• Experience designing relational and dimensional data models.
• Strong understanding of data warehousing and modern data platform architectures.
• Advanced SQL skills and understanding of database design principles.
• Experience working with large-scale enterprise data environments.
• Strong analytical, problem-solving, and communication skills.

Preferred Qualifications

• Experience with AWS Iceberg, Snowflake, Databricks, Redshift, or similar cloud data platforms.
• Experience using data modeling tools such as Erwin, ER/Studio, PowerDesigner, Enterprise Architect, or equivalent.
• Strong understanding of Data Vault, Kimball, and dimensional modeling methodologies.
• Experience supporting Power BI, Tableau, Looker, or other BI platforms.
• Knowledge of Master Data Management (MDM), Metadata Management, and Data Governance frameworks.
• Experience within Payments, Financial Services, FinTech, Risk, or Fraud domains.
• Exposure to AI/ML data requirements and feature engineering concepts.

Technical Skills

• Data Modeling (Conceptual, Logical, Physical)
• Dimensional Modeling
• Data Warehousing
• SQL
• Data Architecture
• Metadata Management
• Data Governance
• Master Data Management
• ETL/ELT Concepts
• Cloud Data Platforms
• Database Performance Optimization
• Data Quality Frameworks

Key Competencies

• Strong Business Acumen
• Stakeholder Management
• Analytical Thinking
• Problem Solving
• Collaboration & Teamwork
• Attention to Detail
• Communication Skills
• Documentation Excellence
• Strategic Thinking
• Continuous Improvement Mindset

Success Measures

The successful candidate will:

• Deliver scalable and reusable enterprise data models.
• Improve data consistency, usability, and quality across platforms.
• Enable faster delivery of analytics and reporting solutions.
• Support governance, compliance, and data stewardship initiatives.
• Reduce data redundancy and improve enterprise-wide data standardization.
• Drive adoption of trusted, well-documented, and governed data assets across the organization.

Ideal Candidate: A collaborative data professional who can bridge business requirements and technical implementation while working effectively with Engineering, Product, Analytics, Data Governance, and Architecture teams to build a trusted data foundation for the enterprise.