Automated Data Quality Development: Design, develop, and implement automated data quality checks for both real-time and batch processing of trading data. Data Pipeline Validation: Monitor and rigorously validate data pipelines supporting trade execution, pricing models, risk analytics, and post-trade settlement processes. Issue Identification & Resolution: Partner with trading desks, quantitative teams, and data engineers to proactively identify, analyze, and resolve complex data anomalies. Data Governance & Traceability: Build and maintain solutions for data profiling, lineage tracking, and metadata management to ensure comprehensive data traceability and auditability. Continuous Integration for Data Reliability: Integrate data validation rules and automated tests into Continuous Integration/Continuous Delivery (CI/CD) pipelines. Root Cause Analysis: Conduct thorough root cause analysis for identified data quality issues and drive the implementation of effective corrective and preventative actions. Regulatory Compliance: Ensure all data quality processes and solutions adhere to global financial regulations and internal compliance standards. Cloud Environment Optimization: Collaborate with DevOps and Cloud Engineering teams to optimize and scale data quality solutions within cloud-based environments (e.g., AWS, Azure, GCP). Advanced Anomaly Detection: Leverage and integrate AI/Machine Learning-based anomaly detection models to proactively identify subtle and complex data inconsistencies. UAT and Product Rollout Support: Provide crucial support for User Acceptance Testing (UAT) processes and the successful rollout of products into production environments, ensuring data quality readiness. Experience: Minimum of 3+years of experience in Quality Assurance, with a strong focus on data quality in backend testing. Backend & API Automation: Strong experience in test automation using Java for backend systems and API testing. Python for Tooling: Hands-on experience in developing automation scripts using Python is a plus. SQL Proficiency: Proficiency in SQL for complex data validation, querying large datasets, and data manipulation. Analytical & Troubleshooting Skills: Exceptional analytical and troubleshooting skills, particularly for debugging and resolving intricate data quality issues. DevOps Integration: Demonstrated experience embedding data quality tests and automation within DevOps CI/CD pipelines. Adaptability: Ability to thrive and contribute effectively in a dynamic, fast-paced trading environment with cross-functional teams. ------------------------------------------------------ Job Family Group: Technology ------------------------------------------------------ Job Family: Applications Development ------------------------------------------------------ Time Type: Full time ------------------------------------------------------ ------------------------------------------------------ For complementary skills, please see above and/or contact the recruiter. ------------------------------------------------------
