Enterprise Data Readiness Leadership. Define and lead the enterprise data readiness strategy supporting AI, analytics, and automation. Establish a prioritized portfolio of enterprise data products (e.g., tenant, lease, unit, property, operations) that are reusable, governed, and decision-ready. Set standards for data definitions, lineage, ownership, and quality across the organization. Ensure consistent entity resolution and interoperability across systems. AI & Decision Enablement. Partner with executive and business leaders to align data products with high-value decisions and workflows. Enable both:AI-driven automation (document intelligence, task support, workflow augmentation)AI-driven decision support (predictions, recommendations, prioritization)Establish criteria for determining when data is appropriate for AI training vs. inference. Data Quality, Ownership & Accountability. Define data quality thresholds aligned to decision risk and materiality, not generic standards. Establish enterprise accountability for data quality, including business ownership and stewardship. Implement practical monitoring and escalation processes to resolve data issues quickly. Governance, Risk & Compliance Partnership. Partner closely with Legal, Compliance, Security, and technical governance teams to ensure:Data usage complies with regulatory, contractual, and internal policy requirements. Access controls, auditability, and retention rules are embedded operationally. Serve as an executive steward of responsible AI data practices. Cross-Functional Executive Partnership. Act as the senior data readiness partner to:Business and operations leadership. IT and AI engineering teams. Analytics and data science. Legal, compliance, and risk functions. Drive alignment and decisions across competing priorities in a matrixed environment. Value Delivery & Scale. Enable early AI and automation pilots and help scale those that demonstrate value. Establish repeatable operating patterns so each new use case is faster, safer, and easier to deliver. Report progress and outcomes using business-relevant metrics that leadership cares about. Required Qualifications. Experience 12 years of experience in enterprise data, analytics, data governance, or related disciplines. Demonstrated success leading cross-functional data initiatives at scale. Experience operating in regulated, compliance-sensitive, or public-company environments strongly preferred. Experience enabling AI, advanced analytics, or decision intelligence capabilities preferred. Leadership Capabilities. Strong “data-as-a-product” mindset with a bias toward execution. Proven ability to influence senior leaders across business, legal, and technology functions. Comfortable resolving ambiguity, tradeoffs, and resistance. Exceptional communication skills with executive presence. Technical Fluency (Not a Coding Role)Familiarity with modern data platforms, integration patterns, and analytics architectures. Understanding of unstructured data management (e.g., contracts, documents) in controlled environments. Working knowledge of how AI systems consume data (training vs. inference, bias, drift, lineage).