Enterprise Architecture Principal – Data & Analytics
Listed on 2026-07-19
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IT/Tech
Data Engineering, AI Engineer (Applied/Software), Data Warehousing, Cloud Computing: Infrastructure & Operations
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 .
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.
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 Data Ops, 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 (Data Ops, 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 .
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…
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