Data Engineer

About the Role We are seeking a Software Developer to join a high-performing enterprise data team responsible for delivering and supporting critical data ingestion capabilities across a large-scale data platform. This role is pivotal in re-architecting legacy data pipelines into modern, scalable systems and building robust backend services that support investment decision-making at scale. This role is ideal for engineers who take pride in clean, well-tested code, and want to play a part in large-scale data transformation . Responsibilities Design and develop scalable Python backend services for data systems. Build and maintain clean, modular, and test-driven code (unit and integration tests using TDD). Design, develop, maintain, and enhance data ingestion pipelines supporting ongoing operational requirements. Ingest and process data from external providers and internal sources into enterprise data platforms. Support BAU operations, including monitoring, troubleshooting, maintenance, and issue resolution for production workloads. Build enhancements to existing data engineering solutions and contribute to continuous platform improvements. Transform legacy data pipelines into modern, maintainable architectures. Collaborate closely with data engineers, backend engineers, and devops engineers for smooth deployments. Ensure high code quality, readability, and maintainability following best practices. Participate in code reviews, technical discussions, and agile ceremonies. Must-Have Skills: 5+ years of experience in backend software engineering, primarily using Python . Strong Python backend development experience. Solid SQL skills and experience working with large-scale datasets. Proficiency in writing unit and integration tests using TDD principles . Experience with Python package management tools (Poetry, Conda, UV, Pip). Experience supporting production data platforms and troubleshooting operational issues. Tech Stack & Tools Languages: Python (required), SQL (required) Database: Snowflake, Databricks DevOps: Kubernetes, Docker, CI/CD Testing: PyTest, TDD practices Nice-to-Have Skills: Familiarity with Snowflake . Working knowledge of Databricks (basic to intermediate level). AWS cloud experience. Kubernetes, Docker, and CI/CD pipelines Financial services or regulated industry experience. Exposure to modern data architecture and cloud-based data platforms. Why Join? Work on large-scale, business-critical data platforms used across the organisation. Gain exposure to modern technologies including Snowflake and Databricks. Participate in platform modernisation initiatives aligned to the organisation's future AI roadmap. Develop expertise in complex external data ingestion and enterprise-scale data operations. Opportunity to make an immediate impact within a high-visibility, production-critical environment. We regret to inform that only shortlisted candidates will be notified EA Registration No: R25158204, Wong Lin, Rachel Allegis Group Singapore Pte Ltd, Company Reg No. 200909448N, EA Licence No. 10C4544