HEAD

The Head of Data Management is responsible for establishing and leading the enterprise data strategy, governance, and quality management. This role ensures that data is managed as a strategic asset and that the organization complies with all relevant data-related regulations and security while also leveraging data to drive business value and innovation​.

1.       Enterprise Data Governance Program: 

• Develop and enforce a comprehensive data governance strategy and framework for the bank.
• Define data policies, standards, and ownership/stewardship models, ensuring alignment with the institution’s overall business strategy​.
• Establish governance processes that are embedded across all business units and functions and ensure that change initiatives and regulatory projects conform to these data policies (e.g. clear data requirements, defined data owners and stewards)​. This includes setting up data governance committees or councils to oversee data management practices.

2.       Data Quality Management:

• Oversee enterprise data quality and reliability.
• Set and monitor Key Performance Indicators (KPIs) for data quality (accuracy, completeness, timeliness) and implement initiatives to continuously improve data across the organization​.
• Manage key data assets and documentation – such as business glossaries, data dictionaries, and data lineage – so that critical data is clearly defined, understood, and trusted by users​.
• Drive data cleansing and validation efforts to remediate data issues and establish processes for ongoing data quality monitoring.

3.       Regulatory Compliance and Risk Data Oversight: 

• Ensure the bank’s data management practices meet all regulatory and compliance requirements.
• Oversee compliance with frameworks and support regulatory reporting needs under Basel II&III and related banking regulations​.
• Ensure robust controls are in place for data privacy and protection in line with laws like Tanzania Data Protection Act.​

4.       AI/ML Data Integration and Governance: 

• Implement and support advanced analytics, machine learning, and AI initiatives by ensuring the underlying data infrastructure and governance can support these use cases. This includes provisioning high-quality, well-governed data for AI/ML model development and establishing guidelines for the ethical and compliant use of AI within the bank.
• Lead data science team to implement AI governance best practices – such as model documentation, bias monitoring, and usage policies – to ensure AI/ML solutions are built on trustworthy data and comply with emerging AI risk management guidelines

5.       Data Architecture and Analytics Enablement: 

• Collaborate with the enterprise architecture and IT teams to design and evolve a modern data architecture that supports business objectives.
• Provide oversight for data platforms (data warehouses, data lakes, etc.) and ensure data architecture decisions align with group-wide standards and the target vision for the bank’s data ecosystem.
• Prioritize and oversee the delivery of data and analytics projects – including business intelligence (BI), reporting, and self-service analytics tools – to ensure they meet governance standards and deliver value.
• Drive the automation of data flows and reporting (e.g. implementing tools for data integration and visualization) to improve efficiency and insight generation​.

6.       Team Leadership and Data Culture: 

• Build and manage the Data Management Office team, including data governance managers, data quality analysts, data architects, and other data management professionals​.
• Provide mentorship and direction to staff, and cultivate expertise in data management practices across the team. Establish clear roles and accountability (e.g. appointing data owners in business units and data stewards for key data domains) and chair a cross-functional data governance council to coordinate enterprise-wide data initiatives​.
• Promote a strong data culture by evangelizing the importance of data across the organization – drive data literacy programs, training sessions on data governance, and other educational efforts to foster awareness and buy-in for data policies​.
• Encourage business units to treat data as a valuable asset and to adhere to governance processes in their daily operations.

7.       Stakeholder Engagement and Reporting:

• Act as the primary liaison for all data-related matters across the enterprise.
• Work closely with business unit leaders, IT, Risk Management, Compliance, and Analytics teams to understand data needs and to ensure coordination on data initiatives.
• Identify and resolve data governance issues by working with stakeholders at operational levels, and escalate critical data issues to executive forums when necessary.
• Provide regular updates and reports on the status of the data management program – including data quality metrics, issue remediation progress, and key successes – to senior management and governance committees.
• Prepare materials for and lead meetings of the Data Governance Steering Committee, using dashboards and reports to communicate progress and challenges​. Through effective communication, keep the Board and C-level executives informed about major data risks, regulatory compliance status, and the business value being realized from data assets.

• Bachelor’s degree in Information Technology, Computer Science, Data Science, Business, or a related field is required. An advanced degree such as a Master’s or MBA in a relevant discipline is strongly preferred​.
• Extensive experience of 10+ years in data management, data governance, advanced analytics or related data-focused roles within the financial services or banking industry, with a leadership experience at a management level driving transformational data initiatives.
• In-depth knowledge of industry-standard data management frameworks and best practices. Familiarity with the DAMA/DMBOK (Data Management Body of Knowledge) principles and other data governance frameworks is required​.
• Deep familiarity with regulatory requirements and standards that impact data in banking. The candidate must have hands-on experience ensuring compliance with reporting and should understand how data quality and governance support regulatory reporting obligations​.
• Knowledge of data privacy and protection regulations is essential, including experience in implementing controls or policies to comply with Tanzania Data Protection Act 2022 and other data privacy laws​.
• Broad knowledge of data architecture and technologies. The candidate should be well-versed in database systems, data warehousing, and data integration (ETL/ELT) processes.
• Experience with big data platforms or cloud data technologies is a plus. Familiarity with business intelligence and analytics tools (such as Tableau, Power BI, SAS, etc.) is expected, that being understanding of data science and AI/ML technologies and how they utilize data​.
• Knowledge of data lineage, metadata management, and data catalog tools in line with good understanding of data security practices and tools.
• Professional certifications such as Certified Data Management Professional (CDMP), Certified Information Management (CIMP), certifications in cloud data platforms or data privacy or equivalent credentials are a plus.

CRDB Bank is dedicated to upholding Sustainability and ESG practices and encourage applicants who share this commitment. The Bank also promotes an inclusive workplace, hence applications from women and individual with disabilities are encouraged.

It is important to note that CRDB Bank does not charge any fees for the application or recruitment process, and any requests for payment should be disregarded as they do not represent the bank’s practices.

Only Shortlisted Candidates will be Contacted.