Job Description

Job Summary

The Assistant Vice President (AVP), Data Quality, will lead QNB's enterprise data quality initiatives to ensure the accuracy, completeness, consistency, and timeliness of critical data assets across the bank. This role supports the bank's strategic goals and regulatory obligations including compliance with Qatar Central Bank (QCB) data mandates, National Data Classification Policy (NDCP), and General Data Protection Regulation (GDPR). The AVP will work closely with business, risk, IT, and analytics teams to establish robust quality metrics, profiling tools, monitoring frameworks, and root cause remediation processes that improve data trust and usability across QNB.


Main Responsibilities

A. Shareholder & Financial: - Design and implement a Group-wide data quality management strategy in line with business and regulatory needs. - Ensure that critical financial and operational decisions are based on high-quality, validated data. - Drive cost savings by minimizing data rework, reconciliation, and reporting discrepancies. - Establish data quality KPIs and benchmarks across key domains including customer, product, and transactions. - Develop and maintain robust data quality controls and auditing mechanisms to ensure continuous data accuracy. - Collaborate with cross-functional teams to identify data quality gaps and implement corrective actions. - Promote a culture of data quality awareness and accountability throughout the organization. - Utilize advanced data profiling and monitoring tools to proactively detect and resolve data quality issues. - Lead the development of comprehensive data quality training programs for employees at all levels. - Support the integration of data quality best practices into new system implementations and upgrades. - Implements KPI's and best practices for Assistant Vice President, Data Quality - Promote cost consciousness and efficiency and enhance productivity, to minimise cost, avoid waste, and optimise benefits for the bank. - Act within the limits of the powers delegated to the incumbent.


B. Customer (Internal & External): - Collaborate with data stewards, IT, and business teams to define and implement data quality rules and ownership structures. - Proactively identify data issues that impact customer experience, operational performance, or regulatory reporting. - Develop feedback and escalation channels to ensure quick resolution of critical data issues impacting business users. - Establish and monitor data quality metrics to measure effectiveness and drive continuous improvement. - Integrate data quality checks into data processing workflows to prevent the propagation of errors. - Conduct regular data quality assessments and audits to identify potential issues and areas for improvement. - Create and maintain comprehensive documentation for data quality standards and procedures. - Facilitate training sessions to educate employees on the importance of data quality and best practices. - Leverage advanced data analytics and machine learning to detect and predict data quality issues. - Implement automated data cleansing and validation processes to enhance data accuracy and reliability. - To assist customers in all their queries on Bank's product and seek solution to their requests. - Maintain activities in accordance with Service Level Agreements (SLAs) with internal departments/units to achieve improvements in turn-around time. - Build and maintain strong/effective relationships with related departments/units to achieve the Group's objectives. - Provide timely/accurate data to external/internal Auditors, Compliance, Financial Control and Risk when required.


C. Internal (Processes, Products, Regulatory): - Lead the deployment of data profiling, validation, and cleansing tools across business units. - Develop root cause analysis workflows and embed automated checks in ETL/data ingestion processes. - Publish dashboards and scorecards that monitor and track data quality improvements over time. - Integrate data quality requirements into change programs, system migrations, and data modernization efforts.- Collaborate with IT and business stakeholders to define data quality standards and policies. - Implement data stewardship programs to assign ownership and accountability for data quality. - Create data lineage documentation to trace data from source to destination, ensuring transparency and traceability. - Utilize data governance frameworks to establish roles, responsibilities, and processes for managing data quality.


D. Learning & Knowledge: - Promote a data quality culture by building internal awareness and capacity across business and IT functions. - Deliver training and onboarding materials for business users and data stewards on quality expectations and tools. - Maintain up-to-date understanding of regulatory trends, tools, and technologies for data quality management. - Develop metrics and KPIs to measure the effectiveness of data quality initiatives and report on progress. - Implement robust data quality monitoring processes to quickly identify and address issues. - Leverage advanced analytics to enhance data quality and uncover hidden patterns or anomalies. - Foster collaboration across departments to ensure consistent data quality practices and standards. - Establish a centralized data quality team responsible for oversight and continuous improvement efforts. - Regularly review and update data quality policies to reflect changes in business needs and regulatory requirements. - Proactively identify areas for professional development of self and undertake development activities. - Seek out opportunities to remain current with all developments in professional field.


E. Legal, Regulatory, and Risk Framework Responsibilities: - Ensure data accuracy standards meet QCB, GDPR, and NDCP requirements for regulatory reporting and audits. - Maintain audit-ready logs of data quality checks, issue logs, and remediation tracking. - Support risk and control teams by ensuring that data feeding financial, AML, or compliance reporting is reliable and validated. - Lead remediation programs arising from internal or external audits relating to data accuracy and lineage gaps. - Implement data validation rules and automated checks at various stages of the data lifecycle to prevent inaccuracies. - Regularly conduct data quality assessments to identify and rectify data inconsistencies. - Collaborate with data owners to establish and enforce standard data definitions and naming conventions. - Develop and maintain a comprehensive data dictionary to enhance data transparency and usability.


F. Other: - Ensure confidentiality, availability, and integrity of data while performing validation or issue management processes. - Maintaining utmost confidentiality concerning customer and internal bank information obtained during the course of business and provide such information on a need to know basis only to Senior Management of QNB, Audit and Compliance functions, and relevant Regulators. - Maintain high professional standards to uphold QNB's reputation and to strengthen its market leadership position. - All other ad hoc duties/activities related to QNB that management might request from time to time.


Education and Experience RequirementsBachelor's or master's degree in data management, Computer Science, Engineering, or Business Analytics.
- 10+ years of experience in data quality, data operations, or data management roles, preferably in the banking sector. - Strong understanding of QCB data accuracy mandates, NDCP classifications, and GDPR obligations.


Hands-on experience with data quality tools and practices such as profiling, cleansing, rule definition, and monitoring.
- Proven track record of leading cross-functional data quality initiatives and influencing at all levels of the organization. - Experience in creating and managing data quality scorecards and dashboards.


Ability to conduct root cause analysis and implement corrective actions for data quality issues.
- Knowledge of best practices in data governance and data stewardship. - Expertise in developing and maintaining data quality metrics and KPIs. - Understanding of data quality implications in regulatory reporting and compliance.