Architecture Principal – Data & Analytics - HIH - Evernorth

The Architecture Principal – Data & Analytics is a Band 5 Senior Contributor responsible for defining and advancing the enterprise data and analytics architecture strategy, target states, and multi-year roadmaps across the organization. This role serves as a recognized architecture authority , setting the northstar vision for modern data platforms, analytics, and AI-enabled capabilities—translating business priorities into pragmatic, scalable, and future-ready architectures . Operating with high enterprise influence and no direct reports, this role requires strong executive presence, the ability to influence without authority, and a relentless focus on enabling data-driven decision making, hyper-personalization, and AI-first innovation . Key Outcomes • Enterprise Data & Analytics Vision: Define and publish a business-aligned data and analytics architecture strategy with a clear 3–5 year roadmap covering data platforms, governance, analytics, AI/ML, and integration layers. • Scalable & Reusable Data Foundations: Establish modern, modular data architectures that reduce fragmentation, promote reuse, and enable enterprise-scale analytics and AI. • Decision Velocity through Architecture Governance: Implement architecture principles, guardrails, and decision frameworks to improve speed, consistency, and quality of analytics and data platform decisions. • AI-Ready & Insights-Driven Enterprise: Enable analytics-ready data, semantic layers, and AI capabilities that accelerate insights, automation, and intelligent decisioning. • Cross-Domain Alignment: Act as a unifying architecture leader across Digital, Data, AI, Security, and Business domains to ensure alignment on key investment and design decisions. Key Responsibilities Strategy & Architecture Leadership • Define and evolve enterprise data & analytics architecture strategy, capability maps, and target-state designs . • Develop and maintain multi-year roadmaps aligned to business outcomes, cost optimization, and innovation priorities. • Establish a data-as-a-product and platform-based operating model . Data Platform & Analytics Architecture • Architect modern data platforms (cloud data lakehouse, streaming, real-time pipelines, semantic/ontology layers). • Define patterns for data ingestion, transformation, storage, access, and consumption . • Enable self-service analytics , BI, and advanced analytics capabilities across the enterprise. • Lead architecture for AI/ML platforms, feature stores, and model lifecycle integration . Data Governance, Privacy & Compliance • Embed data governance, quality, lineage, metadata, and stewardship frameworks into architecture design. • Ensure architectures support regulatory, privacy, and security requirements by design . • Establish patterns for trusted, secure, and compliant data usage . Innovation & Emerging Capabilities • Drive innovation in AI/ML, GenAI, agentic analytics, and intelligent automation . • Evaluate and guide adoption of emerging technologies (e.g., knowledge graphs, semantic models, real-time decisioning). • Lead proof-of-concepts and pilot initiatives for next-generation analytics capabilities. Governance & Decision Enablement • Provide architecture leadership for high-impact data and analytics initiatives .   • Establish reference architectures, reusable patterns, and standards for enterprise adoption. • Drive alignment across portfolios to reduce duplication and optimize investments. Delivery Partnership & Enablement • Partner with engineering, product, and data teams to ensure architectures are executable, scalable, and cost-efficient . • Enable adoption of modern engineering practices (DataOps, MLOps, CI/CD for data). • Support vendor/platform strategy (e.g., lakehouse, cloud, third-party integratio