MdotM

• Develop, maintain, and optimize scalable Spring Boot application • Implement clean, modular, and testable Java code • Contribute to design and implement features following relational (MySQL) and NoSQL (MongoDB) databases • Implement and follow best practices in software architecture, leveraging Layered Architecture, DDD, or Event-Driven design • Troubleshoot and optimize backend performance and security • Work with DevOps teams to improve CI/CD automation and deployment processes

• Develop, maintain, and optimize scalable Spring Boot application • Implement clean, modular, and testable Java code • Contribute to design and implement features following relational (MySQL) and NoSQL (MongoDB) databases • Implement and follow best practices in software architecture, leveraging Layered Architecture, DDD, or Event-Driven design • Troubleshoot and optimize backend performance and security • Work with DevOps teams to improve CI/CD automation and deployment processes

• Design, build, and maintain production-grade data pipelines and data-intensive systems • Configure, schedule, and monitor data pipeline execution to ensure reliability, maintainability, and timely delivery across all data processes • Deploy and manage data infrastructure on AWS or on-premises, ensuring scalability, security, and cost-efficiency • Monitor and optimize relational and NoSQL database performance (e.g., MySQL, PostgreSQL, MongoDB) to ensure efficient querying, indexing, and data access at scale • Ensure reliable ingestion, transformation, and availability of large-scale and time-series financial data, considering financial-specific data quality characteristics • Implement and maintain data quality, validation, and monitoring mechanisms across data workflows • Define and evolve data models and storage structures across relational and NoSQL systems • Collaborate with Data Scientists and Analysts to ensure accurate and efficient data access for analytics and modeling use cases • Optimize data processing workflows for performance, reliability, and operational stability • Troubleshoot complex data issues and drive root cause analysis • Contribute to technical decision-making and help define the roadmap for data infrastructure.