Sr Director, Enterprise Data and AI Platform

About the Opportunity

We are seeking an experienced and visionary Sr. Director, Enterprise Data and AI Platform to define and execute the strategy, architecture, and delivery of Dayforce's enterprise data, AI, and contextual intelligence capabilities. This leader will drive the evolution of our modern data ecosystem, enabling trusted, scalable, and AI-ready data solutions that support business functions across Sales, Marketing, Finance, HR, Customer Success, Support, Services, Legal, and other enterprise organizations.

This role is responsible for architecting and evolving our Enterprise Data Foundation on Microsoft Fabric and Azure, enabling advanced analytics, machine learning, and enterprise reporting. In addition, this leader will architect and build the Enterprise Context Layer—a trusted semantic and knowledge platform that unifies business context, metadata, governance, permissions, and enterprise knowledge to power analytics, intelligent automation, AI agents, and next-generation conversational experiences.

As part of this transformation, the role will lead the strategy and implementation of the Enterprise Agentic AI Platform, enabling employees to interact with enterprise data through secure, AI-powered conversational experiences that complement and, over time, transform traditional dashboards and reporting.

The successful candidate will lead a global organization of data, AI platform, and engineering professionals, including managers, direct reports, and external partners. This highly strategic yet hands-on leadership role requires deep technical expertise, strong architectural vision, exceptional cross-functional leadership, and a passion for delivering innovative, AI-driven solutions that accelerate business outcomes.

This position reports to the VP, Enterprise Data, Analytics and Governance.

• Lead and grow the global Enterprise Data, AI Platform, and Context Engineering organization.
• Define the long-term strategy and roadmap for the Enterprise Data Foundation, Enterprise Context Layer, and Enterprise AI Platform.
• Own the architecture, execution, and evolution of enterprise data, semantic, knowledge, and AI platforms.
• Lead delivery of large-scale, cross-functional data engineering and AI transformation initiatives.

• Lead enterprise data engineering, ingestion, transformation, semantic modeling, and data product development.
• Design scalable, secure, and governed data platforms supporting analytics, AI, and operational workloads.
• Ensure high availability, performance, scalability, and operational excellence across enterprise data platforms.

• Architect and build the Enterprise Context Layer, including semantic models, business knowledge, metadata, permissions, and enterprise context services.
• Drive enterprise standards for semantic modeling, metadata management, knowledge architecture, and reusable context services.
• Enable trusted business context that powers analytics, AI agents, intelligent automation, and enterprise applications.

• Define the enterprise AI platform strategy, architecture, and technology roadmap.
• Lead implementation of Agentic AI platforms, orchestration frameworks, RAG solutions, vector search, MCP services, and intelligent automation capabilities.
• Lead the strategy, architecture, and delivery of enterprise conversational AI experiences that enable natural language interaction with enterprise data.
• Partner with the Decision Intelligence team to deliver AI assistants that leverage trusted enterprise context and progressively replace traditional dashboards and reports.
• Establish enterprise MLOps and LLMOps practices for machine learning and generative AI, including model lifecycle management, prompt and model evaluation, monitoring, observability, retraining, and responsible AI governance.
• Evaluate emerging AI technologies and establish enterprise AI architecture, governance, and security standards.

• Define and enforce enterprise data governance, security, and AI governance practices.
• Define standards and governance for AI model lifecycle management, including model registry, monitoring, explainability, compliance, and responsible AI practices.
• Ensure data availability, protection, compliance, and SLA performance across enterprise platforms.
• Participate in Architecture Review…