Descriptions

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.

Responsibilities

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

Requirements

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

• 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.
• 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
• 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
• 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
• 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

• 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

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