Senior Data Architect

Tokio Marine HCC•Kennesaw, GA

2d•Onsite

About The Position

The Data Architect will help design and enable the future-state enterprise data platform for the Medical Stop Loss business. The role defines architecture, data models, integration patterns, and technical standards for a modern enterprise data warehouse and analytics ecosystem built on Microsoft Fabric and Azure. It combines enterprise data warehousing expertise with modern cloud data architecture experience and works closely with business SMEs, Data & Analytics, Application Engineering, Enterprise Architecture, and other technology teams to make data integrated, standardized, governed, and available for reporting, analytics, AI, and operational decision-making. The position is located onsite in Kennesaw, Georgia. The posting also lists a hybrid work schedule among the company’s offerings.

Requirements

Minimum of 4 years of Data Architecture experience.
5+ years of experience understanding and hands-on implementation of enterprise data warehousing principles, architecture, design patterns, and best practices.
5+ years of demonstrated experience designing and implementing enterprise data warehouses from ingestion through consumption, including 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, and determining 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 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 for 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 model, pipeline, 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.
Medical Stop Loss or Accident & Health insurance experience.

Responsibilities

Define and maintain 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 enterprise data warehousing principles to create a scalable, maintainable, and extensible data environment.
Lead the design of conceptual, logical, dimensional, and physical data models for 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 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 for enterprise reporting, analytics, self-service BI, and AI/ML use cases.
Provide architectural guidance to Data Engineers and review data models, pipelines, and solution designs for alignment with target-state standards.
Partner with application and solution architects to ensure modern applications and services produce data that can be 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 to 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 technologies and patterns based on business needs, scalability, performance, security, maintainability, and cost.