Lead Data Quality and Governance Analyst – Vice President

About us:

Analytics Information management (AIM) is a global community that is driving data driven transformation across Citi in multiple functions with the objective to create actionable intelligence for our business leaders. We are a fast-growing organization working with Citi businesses and functions across the world.

What do we offer:

USCC Enterprise Data team manages the implementation of best-in-class data quality measurement programs across globe in retail consumer bank. The critical areas we support:

Data Governance:
Standardization of data definitions and ensuring consistency in usage as per definitions across systems/products/regions.
Meta Data Management:
Leveraging data lineage, data discovery initiatives and creation of enterprise level meta data for all retail consumer products
Data Ownership:
Identifying trusted data sources, data owners and consumers across process and products
Issue Management:
Identifying defects and investigating root causes for different issues. Following up with stakeholders and creation of plan for resolution as per SLA
Audit Support:
Identifying cases on control gaps, policy breaches and providing data evidence for audit completion
Data Certification:
Developing procedures on data certification and certifying as per fit for purpose criteria

Expertise

Required:

Data/Information Management Senior Manager is responsible for ensuring the organization's data is accurate, complete, consistent, and reliable to support strategic planning and operational efficiency. This role involves profiling data to identify flaws, authoring data quality rules to prevent issues, monitoring data pipelines, managing metadata and remediate data concerns. This person will also be responsible to design, develop, and deploy scalable AI-powered solutions that enhance enterprise workflows and decision-making.

The ideal candidate will combine strong software engineering skills with hands-on experience in machine learning and generative AI systems, including LLM-based applications and AI agents.

Metadata Management and Data Governance

Maintain Data Catalog/Dictionary:
Document and maintain business, technical, and operational metadata, including data lineage, definitions, and data standards.
Data Lineage Mapping:
Utilize metadata to map data lineage, understanding how data flows from source systems to downstream reporting to identify potential impact areas.
Policy Compliance:
Ensure all data assets adhere to defined data governance policies and data privacy regulations.

Data Profiling and Analysis

Profiling Execution:
Perform deep profiling of large datasets to understand data structure, patterns, and content, identifying hidden anomalies or missing information.
Root Cause Analysis (RCA):
Investigate data quality issues to determine the root cause, distinguishing between upstream processing errors and data entry errors.
Data Assessment:
Evaluate critical data elements (CDEs) for accuracy and completeness.

Data Quality Rule Creation and Authoring

Rule Definition:
Collaborate with business stakeholders to define and validate business rules for data validation (e.g., completeness, accuracy, consistency, validity).
Rule Authoring/Implementation:
Develop and implement data quality rules, checks, and preventative/detective controls using SQL, Python, or specialized DQ tools.
Validation Logic:
Document validation logic and exception-handling procedures for critical datasets.

Data Monitoring and Reporting

Continuous Monitoring:
Actively monitor data pipelines, ETL processes, and dashboards to proactively identify DQ issues and operational anomalies.
DQ Dashboards/Scorecards:
Develop and maintain data quality metrics and scorecards to report on data accuracy trends to leadership.
Alerting:
Set up automated alerts for breach of data quality thresholds.

Data Concern Remediations

Issue Resolution:
Identify, document, and triage data quality issues through a tracking system.
Remediation Action Plans:
Develop and execute remediation plans, including data cleansing efforts and automated corrections.

Cross-Functional Collaboration:

Partner with data stewards, IT, and developers to resolve data issues and implement long-term solutions.

(Preferred) –

Design and Develop AI powered solution across data Quality lifecycle utilizing Agentic AI frameworks

Technical Skills

Proficient in Python, SAS, SQL, Teradata, Collibra

Experience with prompt engineering
Experience building LLM-based applications, AI agents, or autonomous workflows
Exposure to Lang Chain / Lang Graph frameworks
Exposure to creating multi-agent orchestration
Exposure to BI tools and technologies – example:
Tableau
Automation and process re-engineering / optimization skills

Domain Skills

Good understanding of

Banking domain (Cards, Deposit, Loans, Wealth management, & Insurance etc.)
Audit Framework
Data quality framework
Risk & control Metrics

(Preferred)  - Knowledge of Finance Regulations, Understanding of Audit Process

Soft Skills

Ability to identify, clearly articulate and solve complex business…