Data Engineer - Collections Analytics

About the role

An experienced Data Engineer is sought to support Digital Collections through data engineering, portfolio analysis and data-driven strategy implementation. The role covers building and optimizing data pipelines, preparing and maintaining collections data, developing MIS reports and analyzing delinquent accounts to improve collection performance. It works closely with Collections, Product, Risk, Technology and Digital Collections teams to strengthen digital collection strategies across UAE, Pakistan and Egypt.

Key responsibilities

• Build and maintain ETL data pipelines using SQL and AWS big data technologies.
• Prepare, maintain and validate collections data for accuracy, quality and accessibility.
• Develop and deliver MIS reports, dashboards and analytical reports.
• Handle ad-hoc data requests and provide insights through portfolio analysis and trend identification.
• Automate manual workflows and improve data pipeline efficiency and scalability.
• Analyze delinquent accounts and support portfolio segmentation and delinquency management.
• Monitor campaign performance and support customer engagement via Email, SMS, IVR, WhatsApp, Voice Blaster and in-app notifications.
• Collaborate with Collections, Product, Risk, Operations and other teams to improve collection processes and customer journeys.
• Ensure data security, privacy and compliance, and support the expansion of digital collections across UAE, Pakistan and Egypt.

Required profile

• Bachelor's degree in Computer Science or a related field; Master's preferred.
• 3-5 years of experience in Retail Collections, Collections Strategy, Credit Operations or Product Management.
• 2-3 years of hands-on experience in data management and MIS reporting.
• Good understanding of collections processes, delinquency management and digital collections strategies.

Required skills

• Strong SQL; Java, Scala or SAS.
• Hands-on ETL tools and knowledge of Hadoop, Spark and cloud platforms (AWS, Azure, Google Cloud).
• Relational databases (SQL Server, Oracle) and NoSQL/in-memory databases.
• Tableau, Power BI or Qlik; Python, R or Spark for analytics and modeling.
• Data architecture, retention policies, infrastructure monitoring and performance optimization.