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. #J-18808-Ljbffr