To design, develop, and manage the Bank’s end-to-end data ecosystem from data warehousing, integration, and governance to advanced analytics and AI modeling ensuring data is reliable, secure, and leveraged for strategic decision-making across all business functions.

Data Architecture & Warehousing

• Design and maintain the Bank’s enterprise data warehouse to support efficient data storage, processing, and access.
• Develop and optimize ETL/ELT processes and data pipelines for seamless integration of data from multiple internal and external sources.
• Ensure data architecture supports scalability, security, and performance requirements.

Data Management & Governance

• Implement and enforce data governance frameworks and standards across the Bank.
• Define and monitor data quality metrics, ensuring accuracy, consistency, and completeness of enterprise data.
• Collaborate with the Cybersecurity and ICT Governance units to ensure data protection, regulatory compliance, and ethical use of data and AI

Advanced Analytics & AI

• Develop, deploy, and manage predictive and prescriptive models that drive actionable insights and automation.
• Oversee the full MLOps lifecycle, including model development, validation, deployment, and performance monitoring.
• Build and operationalize advanced analytics use cases across key business areas such as risk, customer experience, and operations

Business Intelligence & Visualization

• Design and maintain dashboards, reports, and data visualization tools (e.g., Power BI, Tableau) to support executive and operational decision-making.
• Collaborate with business units to translate analytical findings into strategic recommendations

Continuous Improvement & Innovation

• Stay abreast of emerging trends in data science, AI, and big data technologies.
• Recommend and implement improvements to data architecture, tools, and analytical processes.
• Build internal capacity through mentoring and knowledge-sharing sessions

Education

• Bachelor’s degree in Computer Science, Data Science, Information Systems, Statistics, or related field.

Professional Certification

• Professional certifications in Data Engineering, Data Science, Machine Learning, or Business Intelligence (e.g., Azure Data Engineer, Google Professional Data Engineer, Microsoft Power BI Data Analyst).

Experience

• Minimum of 5 years’ experience in data engineering, data analytics, or AI/ML development — preferably in a banking or financial environment.
• Strong hands-on experience with SQL, Python/R, ETL tools, and data warehouse technologies (e.g., MS SQL Server, Snowflake, or equivalent).
• Experience with data visualization tools (e.g., Power BI, Tableau) and cloud data platforms (Azure, AWS, or GCP).
• Working knowledge of MLOps tools and frameworks (e.g., MLflow, Kubeflow, or Azure ML)..

Skills

• Strong analytical and critical thinking skills.
• Deep understanding of data architecture, governance, and lifecycle management.
• Expertise in machine learning, statistical modeling, and data mining.
• Excellent communication and cross-functional collaboration skills.
• High integrity and commitment to ethical AI and responsible data use

• Critical Thinking and Problem Solving Skills

• Team work

• Good Analytical Skills

• intergrity

• Work Ethic & Professionalism Skills

• Communication Skills