Sr Principal Enterprise Data Architect, AI

Key Responsibilities

• Develop, maintain, and communicate the enterprise data architecture blueprint, including data models, platforms, integration patterns, standards, and policies.
• Define and evolve the enterprise data strategy to support analytics, business intelligence, AI/ML, and generative AI initiatives.
• Establish multi-year architecture roadmaps for data platforms and capabilities, balancing modernization, innovation, resiliency, and risk.
• Guide the architectural design and validation of AI-enabled solutions, including analytics accelerators, intelligent assistants, and data-driven platforms.
• Support the transition of successful proofs of concept and pilots into production-ready, enterprise-grade solutions.

Governance & Compliance

• Lead and evolve data governance practices, including metadata management, data quality, lineage, classification, and stewardship.
• Lead Architecture Review Boards to ensure alignment with data architecture standards, best practices, reuse, resiliency, and products/tooling.
• Promote best practices for data management and architectural consistency across the enterprise.

Collaboration & Leadership

• Act as a bridge between business leaders, data owners, architects, engineers, and governance partners.
• Influence architecture, investment, and prioritization decisions through clear communication, evidence, and business impact.
• Mentor and guide architects and senior technologists, raising architectural maturity and consistency across the organization.
• Operate effectively in a global, matrixed environment, aligning diverse stakeholders around shared outcomes.

Solution Architecture & Design

• Arcitect data solutions supporting internal and external applications, analytics, AI/ML, and reporting use cases.
• Define data integration and access patterns, including APIs, pipelines, and event-driven architectures.
• Engage in architecture reviews and design discussions, providing hands-on guidance at critical decision points.

Innovation & Thought Leadership

• Stay current with industry trends in cloud data platforms, AI architectures, data mesh/fabric, and real-time analytics.
• Exposure to industry forums, public speaking, and ability to author architectural guidance, position papers, and reference materials on data and AI as catalysts for business innovation.
• Evaluate emerging technologies and approaches, guiding informed experimentation and adoption.