Job Purpose:Design, build, and maintain scalable data pipelines and infrastructure to support Modupay's intelligence and insights products. Ensure data reliability, availability, and quality across the organization, with a focus on supporting payments and fintech-related data needs.
Key Accountabilities:Develop, and maintain robust data pipelines that ingest, transform, and normalize large volumes of transaction data from multiple sources Implement incremental loading, error handling, retry logic, and pipeline monitoring to ensure SLA-driven data freshness for downstream commercial products. Build unified data models that normalize transaction data across payment schemes and local rails into consistent reporting structures, enabling cross-market and cross-product analytics. Implement data quality, validation, and testing frameworks (data contracts, automated checks, anomaly detection) to ensure integrity of data flowing into analytics products, reporting, and machine learning use cases. Ensure data quality, integrity, and consistency through monitoring, validation, and testing frameworks. Collaborate with data analysts and internal stakeholders to understand data requirements and deliver scalable solutions. Implement and maintain data governance, security, and privacy best practices in compliance with Modupay's policies and regulatory requirements. Identify performance bottlenecks and optimize query performance, data storage, and processing efficiency. Stay current with emerging technologies and industry trends, recommending improvements to the data architecture and tooling
Knowledge and Competencies:
Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field. Master's degree is a plus. Advanced SQL proficiency. Experience with Postgre SQL and Oracle preferred.3 years of experience in data engineering preferably within payments, fintech, or financial services. Strong data modeling skills Hands-on experience with ETL/ELT frameworks and tools Proficiency in Python or another data engineering language for pipeline development, scripting, and automation. Experience with data quality and testing frameworks Exposure to cloud data platforms (AWS, GCP, or Azure) Experience with BI/visualization tool integration Strong analytical and problem-solving skills with attention to data accuracy and reconciliation. Effective communicator, able to translate technical data architecture decisions into business-understandable terms for analysts and product stakeholders. Fluency In English