Vice President-Data Engineer-Risk Models

Job Purpose

To design and maintain batch data pipelines, drive data strategy, and enhance data architecture & best practices. This role requires deep expertise in core banking, retail, digital, marketing, risk, and compliance systems across multiple regions (UAE, Egypt, Pakistan, etc.). The function spans Enterprise Data Lake, Azure Data Lake, BI, Advanced Analytics, and Real-time Streaming platforms. Responsibilities include developing on-prem & cloud data solutions, managing change releases & resources, and delivering critical data insights for strategic decision-making, regulatory compliance, and operational efficiency.

Key result Areas

• Data Engineering & Architecture : Design, develop, and maintain scalable real-time & batch data pipelines to support increasing data demands.
• Data Domains & Governance : Establish data domains across banking products and lay the foundation for Data Mesh architecture while ensuring data security, governance, and compliance.
• Data Platform Management : Oversee ETL processes, data modelling, integration, orchestration, and automation for seamless data operations.
• Collaboration & Business Alignment : Work with analysts, data scientists, and business teams to improve data models, enhance accessibility, and enable data-driven decision-making.
• Production Support & Monitoring : Provide L2 / L3 support, debug data issues, and ensure high availability & quality of production data.
• Risk & Compliance : Implement risk controls, audit reviews, RCSA, and maintain compliance with data privacy regulations through access controls, encryption, and masking.
• Performance Optimization & Scalability : Identify and mitigate bottlenecks, forecast storage & processing needs, and ensure a scalable data infrastructure.
• Incident & Change Management : Coordinate with incident teams, change management boards, and vendors to ensure timely resolutions, approvals, and production deployments.
• Innovation & Best Practices : Stay updated on industry trends, implement emerging technologies, and continuously improve data engineering processes.

Knowledge, Skills and Experience