The RoleThe Senior AI Data Architect Director is a senior, strategy-driven role responsible for designing and evolving the enterprise semantic model that underpins analytics and AI across Presidio. This person sets the architectural direction for how business data is modeled, governed, described, and surfaced, ensuring a single, trusted definition of our metrics and a consistent, AI-ready foundation that both people and AI agents can rely on.
Working at the intersection of the BI and Analytics team, Revenue Operations, Finance, and the AI Enablement function, the AI Data Architect translates business needs into a durable, domain-oriented data architecture spanning Microsoft Fabric, Microsoft Azure, Data Visualization Platforms (e.g., PBI, Tableau), and Salesforce (SFDC). The role pairs strong enterprise data architecture expertise with strong cross-functional partnership and a clear point of view on data governance, semantic standards, and AI enablement. This person works in close partnership with existing data platform leadership to build a deep command of the current Microsoft Fabric and Azure environment and accelerate trusted AI enablement across the enterprise.
Key Responsibilities:AI Semantic Model Strategy- Lead the evolution of the strategy and roadmap for the enterprise AI semantic model architecture, defining how core business entities, metrics, and relationships are modeled for consistent reuse across analytics and AI.
- Design the semantic model to expose governed, well-described data and metric definitions in close partnership with the BI & Analytics Team and cross functional stakeholders into BI tools, AI agents, and downstream consumers so answers are accurate, explainable, and consistent.
- Establish quality standards for data health, performance, and variability to ensure accurate and timely foundational data.
- Curate metadata and business context (descriptions, lineage, ownership, calculation logic) that make the data understandable to both humans and AI.
AI Data Platform & Architecture (Microsoft Fabric & Azure)- Partner with data platform leadership to define the target architecture for scalable, AI-ready data solutions on Microsoft Fabric and Microsoft Azure, covering lakehouse and domain-oriented data product design, modeling, and a Fabric-based semantic model, and provide architectural direction to the engineering teams responsible for pipeline development and implementation.
- Organize data into business-aligned AI ready domains modernizing away from monolithic data warehouses and OLAP cubes toward governed, reusable data domains and data products with clear ownership, context model protocols, and contracts.
- Define reference architectures and patterns for ingestion, transformation, and serving that balance performance, cost, security, and maintainability for AI consumption.
- Guide the data platform roadmap in partnership with data platform leadership and the engineering teams responsible for implementation, performance tuning, and operational excellence.
Analytics & BI Enablement- Connect the semantic model to consumption in Data Visualization platforms, ensuring dashboards and self-service analytics draw from governed, certified data sources.
- Integrate source data into the AI contextual enterprise model so revenue, pipeline, people, cost, and customer data are consistently defined and analytics-ready.
- Partner with BI developers and analysts to reduce duplicate logic and move shared definitions into the enterprise AI semantic model.
Data Governance & Metrics Registry- Partner closely with the Data, BI & Analytics team to establish and lead the enterprise data governance operating model: data quality, lineage, access, certification, and stewardship across the stack, with clear policies, roles, and decision rights.
- Build and support in alignment with the metrics registry as the authoritative catalog of certified business metrics, definitions, owners, and source-of-truth lineage.
- Co-design governance policies and review processes for adding, changing, and deprecating metrics and data models.
- Lead cross-functional governance forums and executive steering committees that set priorities, resolve definitional conflicts, and drive adoption of governance standards across the business.
AI Enablement Strategy- Shape the AI Enablement data strategy ensuring the semantic model makes enterprise data discoverable, trustworthy, and usable by AI applications and agents.
- Define retrieval and grounding patterns (e.g., semantic models, metadata, RAG, certified MCP connectors, and context APIs) that let AI tools answer business questions with governed data, partnering with engineering teams on solution build-out.
- Define enterprise context standards - business rules, policies, and boundaries - that govern how AI applications and agents consume and interpret company data.
- Advise on data readiness for AI use cases, champion responsible AI adoption across the enterprise, and establish guardrails for accuracy, security, and responsible use in accordance with company policies.
Current-State Discovery & Enablement- Build a comprehensive understanding of the current data platform, sources, models, and in-flight initiatives through structured discovery with data platform leadership, engineering teams, and third-party partners.
- Document platform architecture, data flows, and operational practices to strengthen institutional knowledge and continuity.
- Support the alignment of vendor and partner engagements with enterprise data and AI roadmap priorities.
Cross-Functional & Executive Partnership- Lead the development of the enterprise AI and data transformation roadmap, balancing business priorities, technical capabilities, organizational readiness, and measurable business outcomes.
- Collaborate with business partners across the organization to capture requirements, align on definitions, and prioritize the enterprise data and AI roadmap.
- Act as a trusted advisor and evangelist communicating architecture, standards, and trade-offs to both technical and executive audiences.
- Partner with executive leadership on business case development, change management, and communication that support the broader data and AI transformation.
Required Skills and Professional Experience:- Bachelor's degree in Business, Computer Science, Information Systems, or a related business or technical field, or equivalent practical experience.
- 12+ years progressive experience delivering enterprise data, analytics, governance, and business transformation initiatives and 5+ years leadership roles driving data strategy, context layer, revenue operations, or analytics transformation across multiple business functions.
- Significant experience in enabling AI and automation around data architecture, data engineering, or analytics engineering, including designing semantic models and organizing data into governed data domains or data products.
- Demonstrated experience leading enterprise Microsoft Fabric and Azure implementations, including defining architecture, governance, and implementation roadmaps.
- Proven experience delivering analytics with Data Visualization Platforms and integrating enterprise source-system data (e.g., CRM, ERP, HRIS) into analytical models.
- Demonstrated experience leading enterprise data strategy, governance, and cross-functional transformation initiatives that align business priorities with modern data platforms.
- Working knowledge of SQL and semantic modeling sufficient to guide architectural decisions., with a track record of defining certified, reusable metrics.
- Demonstrated experience with data governance: quality, lineage, access management, and stewardship, including establishing governance operating models and standards.
- Demonstrated experience partnering with executive leadership to advance enterprise data and analytics capability through periods of organizational change.
- Excellent communication and stakeholder-management skills, with the ability to partner across AI Enablement, Operations, Finance, BI, and executive stakeholders.
Preferred Skills and Professional Experience:- Experience building AI-ready data foundations and semantic models that support AI/ML, RAG, or agent-based applications.
- Familiarity with semantic model and metrics tooling, and a metrics registry or data catalog.
- Experience applying data mesh or domain-oriented data architecture: data products, domain ownership, and data contracts.
- Experience in Revenue Operations, sales, or finance analytics domains.
- Relevant certifications (e.g., Microsoft Fabric / Azure data certifications, Salesforce, or Tableau).
- Experience establishing enterprise data standards and leading cross-functional governance forums.
- Experience developing enterprise data and AI strategy, building business cases, and evaluating vendors or contractors in support of a transformation program.
Technical Skills Snapshot- Cloud & Platform: Microsoft Fabric, Microsoft Azure (data & analytics services).
- Analytics & BI: Tableau Cloud; certified data sources and self-service enablement.
- Business Systems: ERP, HRIS, CPQ, CRM data integration and modeling.
- Modeling & Query: Semantic/dimensional modeling, SQL, metric definition and certification.
- Data Architecture: Domain-oriented design with governed data domains and data products in place of monolithic warehouses and cubes.
- Governance & AI: Data quality, lineage, metadata/context management, and AI-readiness patterns.
- Strategy & Leadership: Enterprise data and AI strategy, governance operating models, and executive communication.
Leadership- Executive Partnership
- Organizational Change
- Strategy
- Governance
- Roadmapping
- Team Building
