Lead Data Platform Architect

Job Responsibilities and Requirements Serves as the recognized technical expert and architecture lead for a complex enterprise data platform and its connected ecosystem, spanning data lake, data warehouse, integration, semantic, and AI-ready capabilities. Defines the platform's target-state architecture and the reference designs, patterns, guardrails, and nonfunctional requirements that engineering teams build against, and personally designs and resolves the most complex and highest-risk platform architecture problems.

Drives toward a unified, well-modeled view of business subject areas across operational and financial data, establishes architecture standards for data quality, lineage, and observability supporting analytics, machine learning, and generative AI, and evaluates emerging platform and AI capabilities. Remains engaged through implementation and stabilization by leading design and code-review standards for architectural alignment, resolving architecture deviations, and guiding Data Engineering and Platform Engineering & Operations partners, onsite and offshore teams, and vendors, while those teams retain ownership of pipeline delivery and day-to-day platform operations.

Key Responsibilities Owns architecture direction across the major capabilities of the enterprise data platform, including ingestion, storage, distributed processing, data warehouse, integration, metadata, semantic layers, lineage, observability, security, and resilience.

Develops and maintains the platform target-state architecture and transition roadmap, driving toward a unified, consolidated view of business subject areas such as policies, claims, billing, rating, general ledger, and annuities.

Personally designs and resolves the most complex and highest-risk platform architecture problems, including cross-component data flows, platform consolidation, new data-asset ingestion, partner onboarding, and data conversions into new administrative systems.

Defines reusable reference designs, architecture patterns, guardrails, and nonfunctional requirements for scalability, performance, resilience, security, operability, and cost effectiveness that engineering teams apply.

Designs AI-ready data foundations and establishes architecture standards for data quality, lineage, and observability that keep model training and inference data accurate, reproducible, and auditable.

Leads data profiling and conceptual, logical, dimensional, and physical modeling for complex subject areas and sets modeling standards for the platform.

Evaluates emerging platform and AI capabilities, such as vector databases, semantic layers, and LLM-based tooling, through architecture assessment or proof of concept and recommends adoption approaches.

Leads technical design sessions and architecture reviews; reviews high-impact engineering designs and code for alignment with architecture standards and establishes design and coding practices that are maintainable and supportable.

Partners with Platform Engineering & Operations and Data Engineering on capacity, performance, batch run-time, and cloud-cost concerns by identifying architectural causes and defining architectural improvements and refactoring approaches.

Provides architecture leadership for severe or recurring incidents and major defects with architectural causes, including root-cause analysis and remediation plans, and coordinates resolution with engineering and operations teams.

Partners with data governance, security, compliance, and audit teams to ensure platform architecture supports enterprise governance frameworks, security standards, AI-related data use, and financial-reporting controls such as SOX.Serves as architecture point of contact for business stakeholders and IT partners,…