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.