Sr Director, Enterprise Data and AI Platform at Dayforce

Job description

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. What You'll Get to Do Enterprise Data, AI & Context Leadership 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. Enterprise Data Foundation & Modern Data Platform 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. Enterprise Context Layer & Knowledge Architecture 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. Enterprise AI Platform & Conversational Experiences 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. Governance, Security & Architecture 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 Board (ARB) governance and enterprise technology strategy. Manage platform budgets, vendor relationships, and strategic technology partnerships. Skills and Experience We Value 10+ years of experience leading enterprise data, AI platform, or engineering organizations. Demonstrated ability to lead large-scale transformation initiatives and manage complex programs, budgets, and vendor relationships. Strong business partnership, executive communication, and change management skills. Expertise in modern data platforms, including Azure Cloud, Microsoft Fabric, Data Lake/Warehouse/Mesh, data integration technologies, Power BI, and Tableau. Strong understanding of enterprise context management, metadata management, ontology design, and semantic modeling. Knowledge of Agile methodologies, particularly SAFe (Scaled Agile Framework). Skilled at cross-functional collaboration and aligning technology delivery with enterprise priorities. What Would Make You Stand Out Advanced degree in Computer Science, Data Science, Machine Learning, or a related field. Experience designing enterprise semantic layers, knowledge graphs, metadata platforms, and enterprise knowledge management solutions. Experience building enterprise AI platforms utilizing RAG, vector databases, MCP, Agentic AI, and conversational AI architectures. Experience implementing Microsoft Fabric, Azure AI Foundry, Azure OpenAI, Azure Machine Learning, and enterprise MLOps/LLMOps frameworks, including model registry, deployment, monitoring, and governance. Proven experience delivering enterprise conversational analytics and AI-powered business experiences. Demonstrated ability to translate business knowledge into reusable, AI-ready data and context models. Deep understanding of enterprise AI operational practices, including model governance, observability, responsible AI, and continuous delivery of machine learning and generative AI solutions.

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Keywords to mirror

aiazuremachine learningagile

Platform: dayforce
Effort: medium

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