Data Management Analyst

About this role: Wells Fargo is seeking a...

In this role, you will:

• Participate in less complex analysis to identify and remediate data quality or integrity issues and to identify and remediate process or control gaps
• Adhere to data governance standards and procedures
• Identify data quality metrics and execute data quality audits to benchmark the state of data quality
• Design and monitor data governance, data quality and metadata policies, standards, tools, processes, or procedures to ensure data control and remediation for companywide data management functions
• Support communications with basic documentation related to requirements, design decisions, issue closure, or remediation updates
• Support issue remediation by performing medium risk data profiling, data or business analysis, and data mapping as part of root cause or impact analysis
• Provide support to regulatory analysis and reporting requirements
• Recommend plans for the development and implementation of initiatives that assess the quality of new data sources
• Work with business and technology partners or subject matter professionals to document or maintain business or technical metadata about systems, business or data elements, or data-related controls
• Consult with clients to assess the current state of data quality within area of assigned responsibility

• 2+ years of Data Management, Business Analysis, Analytics, or Project Management experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education

• 2+ years of data quality, data analysis, or data management experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, or education.
• Working knowledge of SQL, including querying, joining, aggregating, reconciling, and analyzing structured data.
• Demonstrated ability to investigate data discrepancies, identify patterns, document findings, and reach supportable conclusions.
• Ability to communicate technical or data-related findings clearly in written and verbal form.
• Ability to use current AI and productivity tools, including Microsoft Copilot, and validate AI-assisted outputs before using them in analysis, documentation, or reporting.
• Ability to manage assigned work, maintain accurate records, meet deadlines, and escalate risks or impediments appropriately.
• Experience with data defects, data-quality controls, reconciliations, production support, issue management, or root-cause analysis.
• Knowledge of banking or financial products and Finance, accounting, general ledger, regulatory, risk, or operational data.
• Experience profiling or validating large datasets.
• Understanding of data-quality concepts, including completeness, accuracy, timeliness, validity, consistency, and reconciliation.
• Familiarity with JIRA, ServiceNow, data-quality platforms, workflow tools, or similar issue-management applications.
• Experience preparing defect metrics, dashboards, trend reporting, or management information.
• Familiarity with Python, Power BI, automation tools, or AI-assisted data analysis.
• Understanding of data lineage, metadata, source-to-target mapping, or data transformation.
• Experience working with India- and US-based stakeholders in a global operating model.

• Participate in low-to-moderate complexity data-quality and defect-management initiatives.
• Analyze data-quality control failures, reconciliation exceptions, production issues, and reported data concerns using SQL and other analytical tools.
• Research issues across the data lifecycle to identify affected data, potential root causes, business impacts, and accountable parties.
• Manage assigned defects from intake and triage through remediation tracking, validation, evidence collection, and closure.
• Support the execution, analysis, and enhancement of data-quality rules, controls, tactical adjustments, and remediation solutions.
• Maintain complete and accurate defect records, status information, supporting documentation, and management reporting.
• Prepare defect metrics and trend analysis to identify recurring issues, aging items, remediation barriers, and matters requiring escalation.
• Communicate findings clearly to business and technical stakeholders and collaborate with partners to support timely resolution.
• Use approved AI tools, including Microsoft Copilot, to improve research, analysis, documentation, and productivity while validating outputs before business use.
• Identify opportunities to improve or simplify defect-management processes through standardization, reporting, and automation.