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QNB Group seeks a Vice President of Data Analysis to lead its enterprise analytics strategy, develop executive dashboards, KPI frameworks and BI solutions that drive measurable business value. You will manage a team of analysts and BI professionals and collaborate across units to improve profitability and efficiency while ensuring regulatory compliance.
The ideal candidate will have advanced SQL and Python, strong data modeling skills, DWH/lakehouse design experience, and expertise in ETL/ELT
Established in 1964 as the country’s first Qatari-owned commercial bank, QNB Group has steadily grown to become the largest bank in the Middle East and Africa (MEA) region.
QNB Group’s presence through its subsidiaries and associate companies extends to more than 31 countries across three continents providing a comprehensive range of advanced products and services. The total number of employees is more than 28,000 serving up to 20 million customers operating through 1,000 locations, with an ATM network of 4,300 machines.
QNB has maintained its position as one of the highest rated regional banks from leading credit rating agencies including Standard & Poor’s (A), Moody’s (Aa3) and Fitch (A+). The Bank has also been the recipient of many awards from leading international specialised financial publications.
Based on the Group’s consistent strong financial performance and its expanding international presence, QNB currently ranks as the most valuable bank brand in the Middle East and Africa, according to Brand Finance Magazine.
QNB Group has an active community support program and sponsors various social, educational and sporting events.
The Vice President – Data Analysis will lead the execution of QNB’s enterprise-wide analytics strategy, ensuring insights-driven decision-making across all business units. This role involves overseeing the development of executive dashboards, KPI frameworks, performance analysis, and business intelligence solutions that drive measurable business value. The incumbent will manage a team of analysts and collaborate across functions to improve profitability, enhance operational efficiency, and ensure compliance with internal and regulatory standards.
It is ideal that the candidate should have: Advanced SQL and data modeling, Python, DWH / Lakehouse Design, ETL / ELT Pipeline Development, BI and reporting platforms, Data quality and reconciliation, Banking data domain knowledge, Workflow orchestration and automation, Databricks or similar, Data governance.
KPIs.