Executive Vice President, Data Transformation - Real Assets Data Steward

Fund Administration is Citco’s core business, and our alternative asset and accounting service is one of the industry’s most respected. Our continuous investment in learning and technology solutions means our people are equipped to deliver a seamless client experience.  

As a core member of our Data Transformation team, you will be working with some of the industry’s most accomplished professionals to deliver award-winning services for complex fund structures that our clients can depend upon.

Your Role

The Domain Data Steward serves as the primary bridge between a designated business domain and the Data Transformation Office. This role combines deep subject matter expertise with hands-on data governance accountability, driving the adoption of the firm's data strategy within the domain, identifying and realizing the value of more intentional data use, and ensuring that the domain's data assets are well-understood, trustworthy, and fit for purpose across the enterprise data platform. A key priority of this role is championing an AI-first approach to data stewardship — ensuring that metadata, tagging, and data assets are structured and maintained to support AI knowledge bases, retrieval systems, and automated intelligence workflows across the enterprise.

Strategy & Engagement

• Socialize the Citco’s data strategy within the assigned domain; build awareness, understanding, and sustained buy-in among domain stakeholders and leadership
• Identify and surface data opportunities – process improvements, improved systems integrations, analytical use cases, reporting gaps – that can be unlocked through more intentional data management
• Partner with domain leaders to agree on a clear value proposition and define interim milestones that demonstrate early and ongoing return on investment
• Develop and maintain a current state assessment of the domain’s data landscape (sources, data flows, data quality, gaps) as the foundation for improvement planning
• Author and maintain the domain’s aspirational data roadmap, aligning it with the broader data strategy and enterprise platform initiatives
• Own and maintain the domain's section of the enterprise data dictionary — including field definitions, business rules, data ownership, PII and sensitivity tagging, and contextual usage tips — following the enterprise’s standardized format and tagging conventions to ensure compatibility with AI ingestion, RAG systems, and automated knowledge discovery
• Define and enforce AI-ready metadata standards — ensuring all domain data assets are tagged, classified, and documented in alignment with the enterprise AI knowledge base architecture
• Represent the domain on the Data Governance Committee, contributing domain expertise, sharing lessons learned, and ensuring alignment with the cross-domain governance standards
• Participate in governance forums, steering groups, and working sessions as the authoritative voice for the domain’s data interests
• Lead the domain’s integration with the enterprise Master Data Management (MDM) platform
• Participate in feedback loops for AI-generated outputs within the domain — reviewing accuracy, identifying metadata gaps, and iteratively refining definitions and tagging to improve retrieval quality and response fidelity

Data Modeling & Platform Design

• Collaborate with Data Engineering and Product Owners to design end-to-end reporting data models across the medallion architecture
• Define and document data quality check requirements at each layer (systems of engagement, systems of record, systems of insight, etc.), including business rules, tolerance thresholds, warning and error boundaries, and remediation expectations
• Specify data governance requirements – retention, access controls, classification, and lineage – for the domain datasets

• Define and ratify data contracts with upstream data providers and downstream consumers, ensuring agreed expectations around structure, business rules, quality tolerances, and SLAs are formally documented and maintained

   

Data Quality Management

• Oversee the end-to-end testing of domain data pipelines and datasets, coordinating with Data Engineering on test case design, execution, and sign-off
• Maintain a comprehensive data quality documentation library covering all active checks, tolerances, warning thresholds, error conditions, and remediation procedures
• Continually monitor and improve data quality and data health; own and maintain a data health score for each domain dataset, triage quality incidents, coordinate resolution, and track recurrence to drive root cause elimination, implement additional data quality checks

Communications & Enablement