Enterprise Architecture Principal – Data & Analytics

The Enterprise Architecture Principal – Data & Analytics is a senior technology leader responsible for defining the future enterprise data and analytics architecture strategy, target states, and multi-year roadmaps. This role recognized architecture authority architecture strategies, establishes technology standards, and creates multi-year roadmaps that support business goals and setting the northstar vision for modern data platforms, analytics, and AI-enabled capabilities—translating business priorities into pragmatic, scalable, and future-ready architectures. The individual will be 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. 

Responsibilities

• Develop and lead enterprise data and analytics architecture strategies and roadmaps.
• Design 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. 
• Lead architecture decisions for large-scale data, analytics, and AI initiatives.
• Architect and support modern data platforms (cloud data lakehouse, streaming, real-time pipelines, semantic/ontology layers).
• Partner with engineering, product, data, and business teams to deliver scalable and cost-effective solutions.
• Evaluate emerging technologies and recommend innovative solutions that support business objectives.
• Enable self-service analytics, BI, and advanced analytics capabilities across the enterprise.
• Lead architecture for AI/ML platforms, feature stores, and model lifecycle integration.
• Embed data governance, quality, lineage, metadata, and stewardship frameworks into architecture design.
• Drive innovation in AI/ML, GenAI, agentic analytics, and intelligent automation.
• Provide architecture leadership for high-impact data and analytics initiatives.  
• Establish reference architectures, reusable patterns, and standards for enterprise adoption. 
• Develop reusable architecture patterns, frameworks, and standards for enterprise adoption.
• Support modern engineering practices, including DataOps, MLOps, and automated deployment processes.
• Partner with engineering, product, and data teams to ensure architectures are executable, scalable, and cost-efficient. 
• Provide technical leadership, mentoring, and guidance across the architecture and data community.
• Enable adoption of modern engineering practices (DataOps, MLOps, CI/CD for data).
• Influence strategic technology decisions and drive alignment across multiple teams and business areas.
• Serve as a mentor and thought leader across the architecture and data community. 
• Drive adoption of best practices, patterns, and architectural discipline.

Qualifications

• Bachelor’s degree in Computer Science, Engineering, or a related field.
• 10+ years of experience in enterprise architecture, data engineering, analytics platforms, or related disciplines.
• Strong knowledge of cloud platforms such as Azure, AWS, or Google Cloud Platform (GCP).
• Proven success defining data/analytics architecture strategies, target states, and roadmaps. 
• Experience with modern data platforms, APIs, streaming technologies, and data integration patterns.
• Knowledge of data governance, privacy