The Strategic Ledger program is at the forefront of Citi's finance transformation, dedicated to implementing a new General Ledger and Contract Sub-Ledger solution across all legal entities, countries, and products. Within this critical initiative, the Data Governance team plays a central role. It is responsible for overseeing the implementation of Citi’s Data Operating Model (DOM) and ensuring compliance with Citi Data Governance Policy (CDGP), which establishes the foundational principles for the consistent and controlled creation, maintenance, and use of enterprise data. Our mission is to empower the organization with fit-for-purpose data—accurate, complete, timely, and reliable—which is essential for driving critical remediation efforts and supporting the production of robust financial reports. This encompasses managing global data initiatives across data governance, data quality, and master data management, alongside defining common intake and output templates for all inbound and outbound integrations. Core Competencies Strategic Data Vision: Proven ability to define and articulate a forward-thinking data strategy that seamlessly integrates data into large-scale transformation initiatives, fostering innovation. Data Change Governance: Strong capabilities in leading and governing end-to-end data-related change management processes, ensuring smooth transitions, stakeholder alignment, and adherence to data governance policies throughout program execution. Data-Driven Decision Making: Proficient in establishing and nurturing a proactive, data-driven culture, utilizing advanced analytics, reporting, and visualization tools to empower informed and timely strategic and operational decision-making. Data Governance Framework & Strategy Data Governance Principles: Deep understanding of data governance principles, policies, and best practices, particularly as they apply to financial ledgers and immutable data structures. Framework Development: Ability to design, implement, and manage a comprehensive data governance framework tailored for a ledger system, encompassing data quality, metadata management, and data lifecycle management. Data Model Management: Demonstrated expertise in end-to-end data model design, development, and maintenance within complex financial systems. Strategy & Roadmap: Skill in developing and executing a data governance strategy and roadmap that aligns with business objectives, regulatory requirements, and the unique characteristics of a ledger implementation. Data Quality & Integrity Data Quality Management: Proficiency in establishing and monitoring data quality standards, processes, and metrics for ledger data, including accuracy, completeness, consistency, and timeliness. Data Validation: Experience in implementing and overseeing data validation rules and controls specific to ledger entries and transactions. Reconciliation: Understanding of data reconciliation processes to ensure consistency between the ledger and other systems. Metadata Management & Data Lineage Metadata Strategy: Development and management of a metadata strategy for ledger data, including business, technical, and operational metadata. Data Lineage & Traceability: Capability to define and enforce data lineage requirements, ensuring complete traceability of ledger entries from source to consumption. Glossary & Catalog: Experience in creating and maintaining a data glossary and catalog for ledger-related terms and data assets. Technical Acumen Ledger Technologies: Foundational understanding of the underlying technology of the specific ledger implementation. Data Architecture: Familiarity with data architecture principles relevant to ledger systems, including data modeling, integration patterns, and storage solutions. Data Utilization: Understanding of how ledger data supports analytics, reporting, and business intelligence, ensuring data is fit for purpose. Data Profiling and Analytics Capability Implementation: Design, build, and run data profiling and analytics function, with roles, responsibilities, processes, and controls to drive Strategic Ledger data requirements. Risk and Issue Management: Report and manage risks and issues through the preparation and analysis of large datasets in the Strategic Ledger and associated applications. Insights and Optimization: Leverage data profiling and analytics outputs to identify and drive opportunities to optimize data quality and usage. Experience with Python, Knime, AI/ML, SQL required.