{"id":2895148,"url":"https://alion.io/job/dayforce-sr-director-enterprise-data-and-ai-platform-2","title":"Sr Director, Enterprise Data and AI Platform","company":{"id":175960,"name":"Dayforce","domain":"dayforce.com","url":"https://alion.io/company/dayforce","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Dayforce","truth_index":{"grade":"B","score":80,"open_postings":14,"ghost_share":0,"stale_share":0.786,"repost_share":0,"time_to_fill_p50_days":49,"computed_at":"2026-10-27T05:46:45Z"}},"role":"Leadership","role_family":"Leadership","seniority":"head","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":166000,"max_usd":311000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":2716},"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"AI Agents","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Context Engineering","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"LLMOps","optional":false},{"name":"Machine Learning","optional":false},{"name":"Microsoft Fabric","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"OpenAI","optional":false},{"name":"Power BI","optional":false},{"name":"RAG","optional":false},{"name":"Tableau","optional":false}],"status":"closed","first_seen_at":"2026-08-24T05:00:00Z","employer_posted_date":"2026-10-20","last_verified_at":"2026-10-26T13:11:55Z","board_verified":false,"closed_at":"2026-10-26T13:11:55Z","days_open":63,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":63},"description":"About the Opportunity\nWe 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.\nThis 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.\nAs 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.\nThe 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.\nThis position reports to the VP, Enterprise Data, Analytics and Governance.\nWhat You'll Get to Do\nEnterprise Data, AI & Context Leadership\nLead and grow the global Enterprise Data, AI Platform, and Context Engineering organization.\nDefine the long-term strategy and roadmap for the Enterprise Data Foundation, Enterprise Context Layer, and Enterprise AI Platform.\nOwn the architecture, execution, and evolution of enterprise data, semantic, knowledge, and AI platforms.\nLead delivery of large-scale, cross-functional data engineering and AI transformation initiatives.\nEnterprise Data Foundation & Modern Data Platform\nLead enterprise data engineering, ingestion, transformation, semantic modeling, and data product development.\nDesign scalable, secure, and governed data platforms supporting analytics, AI, and operational workloads.\nEnsure high availability, performance, scalability, and operational excellence across enterprise data platforms.\nEnterprise Context Layer & Knowledge Architecture\nArchitect and build the Enterprise Context Layer, including semantic models, business knowledge, metadata, permissions, and enterprise context services.\nDrive enterprise standards for semantic modeling, metadata management, knowledge architecture, and reusable context services.\nEnable trusted business context that powers analytics, AI agents, intelligent automation, and enterprise applications.\nEnterprise AI Platform & Conversational Experiences\nDefine the enterprise AI platform strategy, architecture, and technology roadmap.\nLead implementation of Agentic AI platforms, orchestration frameworks, RAG solutions, vector search, MCP services, and intelligent automation capabilities.\nLead the strategy, architecture, and delivery of enterprise conversational AI experiences that enable natural language interaction with enterprise data.\nPartner with the Decision Intelligence team to deliver AI assistants that leverage trusted enterprise context and progressively replace traditional dashboards and reports.\nEstablish 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.\nEvaluate emerging AI technologies and establish enterprise AI architecture, governance, and security standards.\nGovernance, Security & Architecture\nDefine and enforce enterprise data governance, security, and AI governance practices.\nDefine standards and governance for AI model lifecycle management, including model registry, monitoring, explainability, compliance, and responsible AI practices.\nEnsure data availability, protection, compliance, and SLA performance across enterprise platforms.\nParticipate in Architecture Review Board (ARB) governance and enterprise technology strategy.\nManage platform budgets, vendor relationships, and strategic technology partnerships.\nSkills and Experience We Value\n10+ years of experience leading enterprise data, AI platform, or engineering organizations.\nDemonstrated ability to lead large-scale transformation initiatives and manage complex programs, budgets, and vendor relationships.\nStrong business partnership, executive communication, and change management skills.\nExpertise in modern data platforms, including Azure Cloud, Microsoft Fabric, Data Lake/Warehouse/Mesh, data integration technologies, Power BI, and Tableau.\nStrong understanding of enterprise context management, metadata management, ontology design, and semantic modeling.\nKnowledge of Agile methodologies, particularly SAFe (Scaled Agile Framework).\nSkilled at cross-functional collaboration and aligning technology delivery with enterprise priorities.\nWhat Would Make You Stand Out\nAdvanced degree in Computer Science, Data Science, Machine Learning, or a related field.\nExperience designing enterprise semantic layers, knowledge graphs, metadata platforms, and enterprise knowledge management solutions.\nExperience building enterprise AI platforms utilizing RAG, vector databases, MCP, Agentic AI, and conversational AI architectures.\nExperience implementing Microsoft Fabric, Azure AI Foundry, Azure OpenAI, Azure Machine Learning, and enterprise MLOps/LLMOps frameworks, including model registry, deployment, monitoring, and governance.\nProven experience delivering enterprise conversational analytics and AI-powered business experiences.\nDemonstrated ability to translate business knowledge into reusable, AI-ready data and context models.\nDeep understanding of enterprise AI operational practices, including model governance, observability, responsible AI, and continuous delivery of machine learning and generative AI 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