Senior Technical Lead

Technology, Data & Digital · Data, AI & Analytics · Data Engineering · Data Science · Business Intelligence

In short

Polaris is looking for a Senior Technical Lead with expertise in data modeling to design and optimize enterprise data models for analytics, BI, AI, and ML. The role involves working with technologies like Kimball, Medallion architecture, Snowflake, Azure, SQL Server, and SAP, and requires defining standards, documenting processes, and collaborating with various teams.

Responsibilities

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.
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.
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.
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.
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.
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.
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 through to consumption layers.

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 stakeholders.

Skills

Data ModelingData WarehousingKimballMedallion ArchitectureSnowflakeAzureSQL ServerETLELTPower BIMicrosoft FabricSAPSQLMDMCDC

#Data Modeler#Data Architecture#Data Warehousing#BI#AI#ML#Snowflake#Azure#SQL Server#Data Transformation#Data Governance#Power BI#Microsoft Fabric#MDM#SAP

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