Data Engineer – Retail Banking

About the role:

The role will be responsible for designing, developing, and maintaining data pipelines, data integration, and data processing solutions that enable Retail Banking teams to effectively utilize data for business insights, customer analytics, performance management, and strategic decision-making.

Key responsibilities:

• Design, develop, and maintain data pipelines and data integration processes to support Retail Banking business requirements.
• Develop and optimize ETL/ELT processes for collecting, transforming, and integrating data from multiple sources.
• Build and maintain scalable data solutions to support reporting, analytics, business intelligence, and advanced analytics.
• Work with business stakeholders to understand data requirements and translate them into effective technical solutions.
• Integrate data from various sources, including core banking systems, lending, deposits, cards, wealth, CRM, digital channels, and other Retail Banking systems.
• Ensure data pipelines are reliable, scalable, secure, and optimized for performance.
• Implement and maintain data quality checks, data validation, and data monitoring processes.
• Troubleshoot data issues and perform root-cause analysis to ensure data accuracy and availability.
• Collaborate with Data Analysts, Data Scientists, Business Analysts, and IT teams to provide appropriate data solutions.
• Support the development and enhancement of data warehouse, data lake, data mart, and other data platforms.

Requirement:

• Bachelor's or Master's degree in Computer Science, Computer Engineering, Information Technology, Data Engineering, Statistics, or a related field.
• Over 5 years of experience in Data Engineering, Data Integration, ETL/ELT, Data Warehouse, or a related field.
• Experience in designing and developing data pipelines and data processing solutions.
• Strong SQL skills and experience working with relational databases.
• Programming experience in Python, Java, Scala, or similar languages.
• Experience with ETL/ELT tools and data integration technologies.
• Understanding of Data Warehouse, Data Lake, Data Mart, and data modeling concepts.
• Experience working with large datasets and optimizing data processing performance.
• Good understanding of data quality, data governance, data security, and data lifecycle management.
• Strong analytical and problem-solving skills.