Data Quality Exception Management Lead Analyst

Citigroup Inc.; Tampa (FL); Sur place; USD 100 000 - 140 000; Plein temps

Résumé du poste

Citigroup Inc. in Tampa, FL, is seeking a Data Quality Exception Management Lead Analyst to drive the road map for data quality exception management within the Enterprise Data Office.

The role combines policy alignment, requirements authoring, and collaboration with business and IT teams to deliver AI-enabled tooling enhancements. You will own requirements for data quality exception management tools, groom JIRA stories, prioritize backlogs, and measure adoption of governance principles while

Qualifications

• 6-10 years of experience in Data Governance/Data Management or related area.
• Experience authoring business and/or functional requirements for development teams.
• Exposure to AI-assisted product development lifecycle (PDLC) is preferred.

Responsabilités

• Understand and adhere to policy-level requirements related to Data Quality.
• Author functional requirements for tool enhancements enabling Data Quality Exception management.
• Document requirements in JIRA and conduct grooming sessions with developers and technical staff.
• Prioritize requirements for release sprints aligning to policy requirements and business needs.
• Work with Citi data stakeholders to automate manual steps and integrate AI-forward solutions.
• Gather and synthesize metrics on data governance practices and report on adoption of principles.

Connaissances

Data governance

Requirements authoring

JIRA management

AI-assisted PDLC

Product Owner experience

Formation

Bachelor's degree

Outils

JIRA

Description du poste

## Data Quality Exception Management Lead AnalystApply: Hybrid: Tampa Florida United States: Irving Texas United States: Full time: Posted Today: 261002091The Data Quality Exception Management Lead Analyst is a senior individual contributor responsible for delivering against the directional strategy of Data Quality Exception Management. This position requires innovative creativity where you will have the ability to analyze both current capabilities and target state models and help build a roadmap that advances the state of data quality exception management systems. The Enterprise Data Office (EDO) maintains tooling that enables businesses across the bank to manage, track, and disposition data quality exceptions that originate from data quality rules. In this role, you will own the requirements for one or more data quality exception management tools. You will work with other data quality professionals to maintain, enhance, and improve the capabilities available within the tools. You will actively solicit and author requirements from the businesses to help improve capabilities and remediate pain points, continuously pursuing simplicity of design and integration with enhanced capabilities, including AI-based features. You will also work with technical teams who will build the enhanced functions to ensure the requirements are understood and properly implemented. This role requires someone who is dynamic, flexible, and can easily respond to quickly changing needs. The candidate must have the ability to handle ambiguity and complexity and be able to deliver on multiple responsibilities. Developed communication and diplomacy skills are required in order to guide, influence and convince others. **Responsibilities:*** Understand and adhere to all Policy-level requirements related to Data Quality.* Partner with multiple teams to build software solutions related to Data Governance policies and/or Data Risk and Control frameworks.* Author functional requirements for the enhancement of tools that enable Data Quality Exception management. Document requirements in JIRA stories and conduct grooming sessions with developers and technical staff.* Prioritize requirements for release sprints that align to changing Policy requirements and business operational needs.* Work closely with data-focused stakeholders embedded within the various businesses across Citi to understand their operational procedures as they relate to data quality exception management. Identify opportunities to automate manual steps, alleviate inefficiencies in existing tooling, integrate AI-forward solutions, and prioritize the product backlog.* Provide expertise on Data Governance and/or Data Risk and Controls framework including design, effectiveness, performance monitoring and self-assessment processes* Oversight and analysis of data-related issues, tracking of ownership and target dates and associated metrics and reporting* Gather and synthesize metrics on existing Data Governance and/or Data Risk and Controls principles, policies, practices and standards, e.g., understanding industry best practices* Produce recommendations for enterprise-wide guiding principles, policies, processes and practices* Determine requirements for and prioritize development of new enterprise-wide principles, policies, processes and practices as needed* Develop and manage approach for measuring adoption of principles, policies, processes and practices **Recommended Qualifications**:* 6-10 years relevant experience in a Data Governance/ Data Management/ Process Engineering or related area* Advanced understanding of Project Management methodologies and tools* Demonstrated experience authoring business and/or functional requirements for implementation by development teams is required.* Exposure to working in an AI-assisted environment, especially with regards to requirement authoring and JIRA management, strongly preferred.* Familiarity with AI-assisted PDLC (Product Development Lifecycle) strongly preferred.* Prior experience as a Product Owner strongly preferred.* Ability to monitor tight deadlines or unexpected requirement changes, with the ability to anticipate and balance needs of multiple stakeholders* Ability to communicate effectively to develop and deliver multi-mode communications that convey a clear understanding of the unique needs of different audiences **Education:*** Bachelor's/University degree, Master's degree preferred•Working Environment: office setting, minimal travelling and working hours in the office may be required during projects. • Effort: minimal physical effort required. High levels of prolonged and intense concentration may be required in front of computer.