Data Engineer (Data Warehouse) - Monee Credit

About Monee

Monee is a leading digital payments and financial services provider in Southeast Asia, with a growing presence in Latin America. Its mission is to better the lives of individuals and businesses in the region with financial services through technology.

Monee delivers a comprehensive range of digital financial products and services, including mobile wallets, payment processing, credit, banking, and insurtech. By leveraging technology and strategic partnerships, Monee aims to drive greater digital financial inclusion, convenience, and accessibility for individuals and small businesses across Southeast Asia and Latin America.

Monee is part of Sea Limited, a global technology company.

• As a Data Warehouse & Automation Engineer, you will sit at the intersection of data warehouse engineering, automated workflows, and regulatory compliance. 
• You will own the design, build, and maintenance of robust Data Warehouse & Data Mart tables, while building automated data pipelines and Reverse ETL workflows to stream critical data back into operational tools and regulatory reporting bodies.
• Collaborate with BI, Regulatory Compliance, and Project teams to translate business and compliance requirements into Data Mart & Business Automation solutions.
• Implement data validation, error handling, and automated data quality checks to ensure accuracy, consistency, and reliability across all warehouse layers.
• Apply software engineering best practices to analytics codebases, optimize database queries, partitioning, and storage to handle growing data volumes efficiently.
• Build and maintain secure, automated data pipelines to format, validate, and push compliance data directly to regulatory portals.

• Bachelor’s degree in Computer Science, Data Analytics or related field with +2 years of professional experience in a Data Warehouse Engineering or related roles
• Advanced SQL skills with focus on query performance tuning and best practices.
• Proficiency in designing robust data warehouse architectures that support high-volume analytics and business reporting.
• Working knowledge of Python focused on scripting, operational automation, and reverse-ETL.
• Proficiency in Apache Spark (PySpark/Spark SQL) for large-scale data engineering.
• Experience with analytics engineering concepts and tools (dbt, Dataform, or equivalent tool) is a plus.
• Good communication skills in English is a must, with the ability to explain insights clearly to non-technical stakeholders.