Assistant Vice President - Data Analytics

About Us With Citi’s Analytics & Information Management (AIM) group, you will do meaningful work from Day 1. Our collaborative and respectful culture lets people grow and make a difference in one of the world’s leading Financial Services Organizations. The purpose of the group is to enable building Citi’s data assets, analyze information, and create actionable intelligence for our business leaders. The Wealth Data Strategy team partners across Business, Technology, Data, Governance, and Analytics to create scalable data solutions. We work on large-scale data transformation initiatives that bring together fragmented data across regions, businesses, and source systems into trusted, consumption-ready data foundations for the Wealth organization. Position We are hiring an Assistant Vice President (C12) – Data Management/Information Sr Analyst to join the Wealth Data Strategy team. This is an exciting opportunity to play a dual Product Owner / Business Analyst role in One Wealth, Citi Wealth’s premier strategic data transformation program. You will help shape how Wealth data is sourced, modeled, governed, validated, and consumed across the global organization. The role requires a strong mix of business acumen, data knowledge, stakeholder management, and Agile execution. You will work closely with business stakeholders, technology teams, data modelers, governance partners, and downstream consumers to translate business needs into high-quality, governed data products. Key Responsibilities • Product Ownership & Data Product Delivery: Own and drive data product requirements from discovery to delivery. Define scope, prioritize requirements, manage backlog, track delivery and ensure the final output meets business and consumption needs. • Business Analysis & Requirement Management: Partner with business stakeholders to understand objectives, reporting needs, metrics, operational processes and pain points. Translate business requirements into clear functional, data and technical requirements. • Hands-on Data Analysis: Use SQL, Python or similar tools to analyze datasets, validate source data, perform data profiling, investigate issues, create metrics and support business/data analysis. • Source Data Discovery & Mapping: Work with source-system owners, technology teams and domain teams to identify required datasets, attributes, business definitions, lineage, refresh frequency, source availability and data gaps. • Source-to-Target Mapping: Support detailed mapping of source attributes to target data models. Work with Tech BAs, data modellers and engineering teams to clarify mapping logic, transformation rules, derivations and dependencies. • Data Model & Data Dictionary Review: Review logical models, physical models, data dictionaries and DDLs. Ensure the model supports agreed business metrics, reporting needs, analytics use cases and cross-domain consumption. • Metrics & Reporting Enablement: Define and validate business metrics, KPIs and data requirements for dashboards, reports and analytics use cases. Partner with BI/reporting teams to ensure data is fit for consumption. • Data Quality & Production Validation: Define validation approach, data reconciliation checks, completeness checks, DQ rules, test scenarios, PAT/UAT support and production validation. Investigate data quality issues and drive closure with technology/domain teams. • Data Governance & Controls: Support data classification, sensitivity review, cross-border considerations, access controls, governance approvals and data ownership processes for data movement and consumption. • AI & Automation Enablemen t: Identify opportunities to use AI and automation for data discovery, mapping, data quality rule creation, SQL generation, production monitoring, documentation and knowledge management. • Stakeholder Management: Act as a trusted bridge between Business, Technology, Data Modelling, Governance, source-system teams and downstream consumers. Drive meetings, follow-ups, issue resolution and clear communication across global teams. • Documentation & Executive Communication: Create and maintain clear documentation including requirements, user stories, data mappings, business definitions, data gaps, status updates, issue logs, executive summaries and presentation materials. • Mentorship & Collaboration: Support junior team members by sharing domain, data and delivery knowledge. Promote ownership, collaboration, continuous improvement and strong delivery discipline within the team. Key Qualifications • Education : Bachelor’s degree in computer science, Information Technology, Business Administration, Data Analytics, Engineering, Finance or a related field. • Experience : Minimum 7-8+ years of experience in Data Management, Business Analysis, Product Ownership, Data Strategy, Analytics, Business Intelligence, Data Governance or related roles. Prior experience in Banking, Wealth Management or Financial Services is preferred. • Product Owner / Business Analyst Experience: Strong experience working as a Product Owner, Business Analyst, Data Analyst or Data Product Lead, including requirement gathering, backlog management, stakeholder engagement, delivery tracking and business sign-off. • Strong SQL Skills: Hands-on experience writing SQL queries for data analysis, profiling, reconciliation, metric creation, validation and issue investigation. • Python / Analytical Programming: Working knowledge of Python or similar programming/scripting tools for data analysis, automation, validation or reporting support. • Data Platform Knowledge: Good understanding of data warehouses, data lakes, ETL/ELT pipelines, metadata, lineage, source-to-target mapping, data models and data quality concepts. Experience with platforms such as Snowflake, Hadoop/Hive, Teradata or similar technologies is preferred. • BI & Reporting Knowledge: Hands-on experience or strong working knowledge of BI/reporting tools such as Tableau, Power BI, Qlik, Dataiku or similar platf