The Data Engineer role is integral to transforming the Bank into a Data Driven Organization (DDO). This position involves leading and managing multiple data and analytics squads — including Advanced Analytics (AA), Environmental, Social & Governance (ESG), Corporate Credit & Limit Management (CCLM) Data, and Marketing Campaign Data & Analytics — to ensure the delivery of scalable, compliant, and business-aligned solutions. The role requires defining the strategic roadmap, driving AI-enabled innovation, embedding strong governance and data quality frameworks, and facilitating data-driven decision-making across corporate banking.
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Key Responsibilities
- Cluster Leadership & Strategic Direction: Lead multiple squads across Advanced Analytics (AA), ESG Data, and Corporate Credit & Limit Management (CCLM) Data, ensuring cohesive execution of high-impact, enterprise-scale programs.
- Digital Transformation Programs: Oversee the execution of strategic programs for corporate digital banking across regions, covering solutions for Trade Finance, Liquidity Management, Payment Systems, and Collections.
- Analytics and Business Intelligence: Deliver end-to-end BI solutions and operational reports that support executive-level decisions, emphasizing timeliness, accuracy, and actionable insights.
- Data Engineering and Technology Leadership: Manage data architecture, big data engineering, and AI-driven models to unlock new insights and resolve complex business challenges.
- Cross-Functional Collaboration: Work with core banking, risk, compliance, and digital transformation teams to maintain a unified, agile, and scalable data model supporting GTB.
Required Skills
- 14+ years of experience in data engineering, analytics, and enterprise data platforms across on-premise (Hadoop, Cloudera) and cloud (Azure, Databricks) environments.
- Functional expertise in corporate banking data domains including trade, payments, liquidity, revenues, and risk.
- Strong understanding of data governance, compliance, and quality frameworks.
- Knowledge of AI/ML applications in data engineering, predictive analytics, and automation.
- Proficiency in real-time processing (Kafka, Spark Streaming) and graph databases (Neo4j).
Qualifications
- Proven experience in delivering multi-domain data and analytics programs in financial services.
- Significant leadership experience with a successful track record in regulatory-critical program delivery (ESG, credit risk, marketing governance).
- Recognized for driving innovation and achieving measurable revenue and efficiency gains.
Summary
Connect and model complex distributed data sets to build repositories, such as data warehouses and data lakes. This will involve managing data across small to large data sets, structured/unstructured or streaming data, extraction, transformation, curation, modeling, building data pipelines, and selecting appropriate tools, while also writing SQL/Java/Scala code as needed.
Location
United Arab Emirates
