Senior Data Architect

Tokio Marine HCC•Kennesaw, GA

5d•Hybrid

About The Position

The Data Architect will play a key role in designing and enabling the future-state enterprise data platform for the Medical Stop Loss business. The role will define the architecture, data models, integration patterns, and technical standards for a modern enterprise data warehouse and analytics ecosystem built on Microsoft Fabric and Azure. This role requires strong enterprise data warehousing expertise combined with modern cloud data architecture experience. The Data Architect will work closely with business SMEs, Data & Analytics, Application Engineering, Enterprise Architecture, and other technology teams to ensure data across key business domains is integrated, standardized, governed, and readily available for reporting, analytics, AI, and operational decision-making.

Requirements

Deep understanding and hands-on implementation experience with enterprise data warehousing principles, architecture, design patterns, and best practices.
Demonstrated experience designing and implementing enterprise data warehouses from ingestion through consumption, including data integration, transformation, modeling, historical data management, and analytical delivery.
Strong understanding of dimensional modeling, including facts, dimensions, star schemas, conformed dimensions, slowly changing dimensions, surrogate keys, and historical data management.
Experience designing modern data warehouse, lakehouse, and medallion architectures, with the ability to determine when and where different patterns should be applied.
Strong knowledge of Microsoft Fabric, including OneLake, Lakehouse, Warehouse, Data Factory, and Power BI capabilities.
Strong experience with Azure data technologies and cloud-based data architectures.
Strong understanding of ETL/ELT patterns, incremental data processing, change data capture, data reconciliation, and integration of heterogeneous source systems.
Strong SQL skills and understanding of data engineering practices, performance optimization, scalability, and workload management.
Experience designing data architectures supporting enterprise reporting, semantic models, self-service BI, advanced analytics, AI/ML, and GenAI use cases.
Strong understanding of data quality, metadata management, lineage, governance, security, privacy, and master/reference data concepts.
Experience establishing reusable data architecture patterns, development standards, naming conventions, and data modeling practices across engineering teams.
Ability to translate complex business concepts and processes into sustainable enterprise data models and architecture.
Experience providing architectural guidance and conducting data models, pipelines, and solution design reviews with Data Engineering and BI teams.
Experience modernizing legacy data warehouses and migrating analytical workloads to modern cloud-based data platforms.
Strong collaboration skills across business SMEs, Data Engineering, BI, Application Engineering, Enterprise Architecture, and technology leadership.

Nice To Haves

Experience in insurance, healthcare, financial services, or another data-intensive and regulated industry preferred.
Medical Stop Loss or Accident & Health insurance experience is a plus.

Responsibilities

Define and maintain the target-state architecture for the enterprise data warehouse and broader data platform using Microsoft Fabric and Azure.
Establish architectural patterns and standards for data ingestion, transformation, storage, modeling, integration, and consumption.
Apply established enterprise data warehousing principles and best practices to create a scalable, maintainable, and extensible data environment.
Lead the design of conceptual, logical, dimensional, and physical data models supporting key Medical Stop Loss business domains.
Define approaches for integrating data from Policy Administration, Claims, Underwriting, external partners, and other operational systems.
Establish patterns for handling source-system differences, data standardization, incremental processing, historical data, change data capture, and evolving source structures.
Partner with business SMEs and Data & Analytics teams to establish common business definitions, canonical entities, relationships, and data rules.
Design warehouse structures and semantic models that effectively support enterprise reporting, analytics, self-service BI, and AI/ML use cases.
Provide architectural guidance to Data Engineers and conduct data model, pipeline, and solution design reviews to ensure alignment with target-state standards.
Partner with application and solution architects to ensure modern applications and services produce data that can be effectively integrated into the enterprise data ecosystem.
Define and promote standards for data quality, reconciliation, metadata, lineage, governance, security, privacy, and appropriate access to sensitive information.
Establish reusable data architecture patterns and standards that reduce duplication and improve consistency across business domains and engineering teams.
Support modernization and migration of existing data warehouses, databases, reporting solutions, and data pipelines into the future-state architecture.
Evaluate Microsoft Fabric and Azure capabilities and recommend appropriate technologies and patterns based on business needs, scalability, performance, security, maintainability, and cost.