Data Quality Lead

Role Overview

Nair Systems is seeking a Data Quality Lead for its Qatar operations.

The role leads enterprise data quality governance, monitoring, issue management, regulatory reporting, and data quality enablement for AI and analytics.

Qualifications And Experience

• Bachelor's degree in Information Management, Computer Science, Data Analytics, Business Administration, or a related field.
• 8–12 years of experience in data governance, data quality, data management, risk data management, regulatory reporting, or related domains.
• Experience in banking, financial services, or another highly regulated industry is required.
• Strong knowledge of data quality management, data governance, data lineage, metadata management, reference data, and master data management.
• Experience with platforms such as Informatica, Collibra, Microsoft Purview, ABACUS, or equivalent solutions.
  
  

Data Quality Governance and Framework

• Develop, maintain, and enhance the data quality framework, standards, procedures, methodologies, and operating model.
• Define data quality principles, controls, KPIs, KRIs, scorecards, and reporting requirements.
• Align practices with the data governance framework, data management policy, and regulatory expectations, including QCB requirements.
  
  

Oversight and Monitoring

• Define and oversee rules for accuracy, completeness, consistency, validity, timeliness, uniqueness, and integrity.
• Monitor critical data elements across business and support functions.
• Report enterprise data quality performance through dashboards, scorecards, and management reporting.
• Provide data quality trends, risks, and remediation progress to governance committees and senior management.
  
  

Issue Management and Escalation

• Lead issue identification, impact assessment, root cause analysis, remediation tracking, and closure validation.
• Maintain the enterprise data quality issue register.
• Challenge remediation plans from data owners, data stewards, and technology teams.
• Escalate material risks and unresolved issues through governance committees.
  
  

Regulatory and AI Data Quality

• Oversee controls supporting regulatory, financial, risk, compliance, and management reporting.
• Coordinate with Risk, Finance, Compliance, and business units on regulatory data quality.
• Support regulatory examinations, audits, and data-related reviews.
• Assess data quality risks affecting AI models and support controls across the data and AI lifecycle.
  
  

Stakeholder Engagement

• Partner with data owners, data stewards, business units, IT, Risk, Compliance, Finance, Operations, and subsidiaries.
• Promote data quality awareness, accountability, and stewardship across the Group.
• Facilitate working groups and governance forums focused on data quality improvement.