Currently, the Data & Analytics function lacks a formal Data Architecture practice, and this is a key position that will be required as we look to implement a global Data platform solution.
There are currently no established frameworks or foundational processes to support a modern, scalable data environment. Most critically, there is an absence of insurance-specific data modeling expertise, resulting in inconsistent datasets, fragmented ownership of core business entities (such as policy, claims, billing), limited ability to support new initiatives, and ongoing technical debt.
The lack of architectural leadership inhibits efficient ingestion, data quality management, and reliable governance. Strategic Alignment:
Building Data Architecture capability is foundational in consolidating and building a unified data platform with canonical domain models and data management practice with patterns, tools and know-how for efficient analytics, delivery, regulatory reporting, scalability, and enablement of AI use cases across various regions. Expected Outcomes:
Establishment of a ground-up Data Architecture practice.
Deployment of repeatable and scalable patterns for data ingestion, modeling, logging, security, hydration, and management of both functional and non-functional requirements.
Technical ownership of canonical insurance data models (policy, claims, billing, etc.) - providing consistency, extensibility, and adaptability for future business needs.
Selection and implementation of relevant enterprise data frameworks, patterns and tooling to standardize and automate critical data processes. Critical Expectations of the Role:
Bring deep, hands-on experience in insurance data architecture, spanning data warehouse, lakehouse, and cloud-based platforms in support of real-time analytics and AI.
Define, implement, and institutionalize tooling, frameworks, and architectural patterns for the ongoing delivery and upkeep of a global data platform.
Create, own, and evolve canonical enterprise models for all core insurance entities to support operational, analytical, and compliance needs.
Provide strategic technical direction and mentorship, developing a high-functioning Data Architecture practice within the organization.
Partner with business, IT, and compliance teams to ensure all data solutions address current and emerging regulatory, security, and business requirements. Conclusion:
The absence of a foundational Data Architecture practice is a critical gap that we will need to address within Starr Technology organization as we look to start our Data Platform initiative
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