Job Summary
Polaris is seeking a Data Modeler to design, document, govern, and optimize enterprise data models that support analytics, reporting, self-service business intelligence (BI), artificial intelligence (AI), and machine learning (ML) capabilities. This role requires strong experience with Kimball data warehousing, Medallion architecture, Snowflake, Azure-based data transformation patterns, legacy SQL Server analysis, enterprise data modeling, integration patterns, and change data capture (CDC) concepts. Knowledge of Inmon and Data Vault modeling concepts are preferred.
This position is responsible for defining and maintaining data models for the Enterprise Data Warehouse (EDW), curated data products, semantic models, and business-facing analytics layers. The Data Modeler will work closely with data engineering, business intelligence, business analysis, data governance, and application teams to translate business processes and source system structures into scalable, reliable, and well-documented data models.
The Data & Analytics team includes Data Engineering, Business Intelligence, Data Science, Master Data Management, and AI. The Data Modeler will support these teams by establishing modeling standards, preparing bus matrices and source-to-target documentation, defining facts and dimensions, supporting Snowflake implementation patterns, and aligning curated datasets with Power BI semantic models and Microsoft Fabric capabilities. This role also includes defining data quality rules, aligning MDM and reference data requirements, and supporting domain modeling across manufacturing, customer, dealer, and SAP-enabled business processes.
The successful candidate will have strong data modeling and data warehousing experience, a practical understanding of enterprise data architecture and analytics patterns, and the ability to communicate effectively with technical and business stakeholders. This individual must be able to document decisions clearly and ensure that data models are aligned with business needs, governed, performant, and ready for implementation
Key Responsibilities
Data Modeling & Warehouse Design • Design conceptual, logical, and physical data models for enterprise analytics, reporting, and semantic consumption layers. • Apply Kimball dimensional modeling techniques, including conformed dimensions, fact tables, grain definition, slowly changing dimensions, and star/snowflake schemas. • Align Bronze, Silver, and Gold layer designs with Medallion architecture principles and business-ready analytical outputs. Source Analysis, Bus Matrices & ST Documentation • Analyze source systems, business processes, subject areas, and reporting requirements to identify facts, dimensions, hierarchies, relationships, and measures. • Prepare and maintain enterprise bus matrices that map business processes to conformed dimensions and analytical subject areas. • Create source-to-target documentation that includes source fields, target attributes, transformation rules, keys, grain, SCD handling, lineage, and validation criteria. • Analyze legacy SQL Server EDW structures, stored procedures, views, data marts, source extracts, and reporting logic to support data warehouse migration and modernization initiatives Partner with business stakeholders and data engineers to validate model assumptions, business definitions, data quality expectations, and transformation logic. • Define data quality rules, validation logic, exception criteria, and reconciliation checks to support accurate, complete, and trusted data products. • Incorporate MDM and reference data requirements into data models, hierarchy management, cross-reference mappings, code sets, and standard business definitions. • Collaborate with BI teams to define bus matrices, measures, and metrics that support business reporting and analytics. Snowflake, Azure & Data Platform Alignment • Design models optimized for Snowflake tables, views, materialized views, tasks, streams, pipes, and incremental processing patterns. • Collaborate with data engineers on Azure Data Factory pipelines, Fivetran ingestion, ELT/ETL transformations, orchestration design, and dependency mapping. • Ensure data models are implementable across Azure Data Lake, Azure Data Factory, Snowflake, Microsoft Fabric, and related cloud data services. • Define modeling patterns that support scalability, auditability, performance, lineage, and governed reuse across domains. • Apply enterprise integration patterns, including full load, incremental load, event-driven ingestion, batch integration, API-based integration, and CDC concepts, when designing source-to-target flows. • Partner with integration and application teams to understand SAP data structures, extract patterns, master data dependencies, and downstream analytical requirements. Power BI, Semantic Models & Analytics Enablement • Design data models that support Power BI reporting, governed semantic models, measures, hierarchies, and reusable analytical datasets. • Work with BI teams to align facts, dimensions, relationships, and measures with reporting needs and self-service analytics standards. • Support Microsoft Fabric and lakehouse consumption patterns by aligning curated models with reporting, semantic, and analytical workloads. Domain & SAP Data Understanding • Model data across manufacturing and customer domains, including product, dealer/customer, order, inventory, production, service, and related operational subject areas. • Interpret SAP source data, master data relationships, reference tables, transactional structures, and integration touchpoints to support enterprise reporting and analytics use cases. Standards, Governance & Collaboration • Establish and maintain modeling standards, naming conventions, data definitions, documentation templates, and reusable design patterns. • Maintain metadata, lineage, business glossary alignment, and traceability from source systems thro
Skill Requirements
- 8+ years of experience in data modeling, data warehousing, data engineering, BI, or related analytics roles
- Experience analyzing legacy SQL Server data warehouses, data marts, stored procedures, views, source extracts, and reporting logic for modernization or migration initiatives
- Strong hands-on experience with Kimball dimensional modeling, including facts, dimensions, conformed dimensions, grain definition, SCD handling, and bus matrices
- Working knowledge of Data Vault modeling concepts including hubs, links, satellites, business keys, historization, and raw/business vault patterns
- Strong understanding of Medallion architecture and the design of Bronze, Silver, and Gold data layers
- Hands-on knowledge of Snowflake objects and patterns including tables, views, tasks, streams, pipes, incremental processing, and performance optimization
- Experience working in Microsoft Azure environments including Azure Data Factory, Azure Data Lake and Microsoft Fabric
- Knowledge of data transformation and ingestion patterns using ADF pipelines, Fivetran, ELT/ETL tools, orchestration frameworks, and source system integration methods
- Experience designing and supporting Power BI datasets, semantic models, measures, relationships, hierarchies, and governed reporting layers
- Ability to prepare detailed source-to-target mapping documents with transformation rules, keys, lineage, data quality checks, and validation criteria
- Ability to define data quality rules, profiling checks, reconciliation logic, exception handling, and validation criteria for enterprise data products
- Strong SQL skills and ability to analyze source data, validate transformations, profile data, and review implementation logic
- Knowledge of data governance, metadata management, business glossary alignment, documentation standards, and model versioning practices
- Knowledge of MDM and reference data concepts including golden records, hierarchy management, survivorship rules, cross-reference mappings, and standardized code sets
- Understanding of enterprise integration patterns and CDC concepts, including batch, incremental, event-driven, API-based, and change-tracking approaches
- Exposure to working with SAP source data, master data, transactional data structures, reference tables, and SAP-enabled business processes.
- Domain knowledge in manufacturing and customer-related data areas such as product, dealer/customer, orders, inventory, production, service, and commercial analytics
Strong communication skills with the ability to explain modeling decisions to technical teams, BI teams, and business stakeholder