QA Automation Engineer (Data)

The Area: The Investment Management group is a global team guided by Morningstar’s investment principles focused on delivering great long-term investment results to help end-investors reach their financial goals.  We use our expertise in asset allocation, investment selection and portfolio construction to create world-class investment strategies leveraging the full resources of Morningstar. The group specializes in multi-asset investing, using building blocks in equities, fixed income and alternative investments to construct robust portfolios.  Through our investment offerings, we serve financial advisers and institutions, and the investors that they serve.   Shift: UK Shift Role Summary:  The Data Quality Automation Engineer will join the Systematic Strategies team in the Research & Investment group. This experienced professional will design and implement automated data quality controls, validation frameworks, reconciliation processes, and monitoring solutions across enterprise-scale data platforms. The ideal candidate combines expertise financial data management and automation engineering, with a passion for building scalable controls that improve data quality and operational efficiency. Key Responsibilities  The successful candidate will  Design, build, and maintain enterprise-scale automated data quality frameworks like Great Expectations, AWS Glue Data Quality, Amazon Deequ, or similar data validation frameworks  Experience implementing and maintaining data quality controls by establishing quality metrics for multi-asset class financial datasets.  Develop automated monitoring controls capable of identifying anomalies, outliers, missing data, stale data, and reconciliation breaks  Validate investment datasets sourced from multiple external providers including Bloomberg, FactSet, Morningstar, LSEG, and other market data vendors  Develop automated source-to-target reconciliation frameworks across ingestion, transformation, and reporting layers  Collaborate with Data Engineering teams to embed data quality controls throughout ETL/ELT pipelines  Support onboarding and robustness checks for new datasets including validation of data completeness, accuracy, consistency, and fitness for downstream investment and research workflows  Requirements:  Advanced Python development  Strong SQL and data analysis skills  Hands-on experience with PySpark and distributed data processing  Experience building automated data validation and reconciliation frameworks  Experience working with large-scale structured and semi-structured datasets in financial domain  Data profiling, anomaly detection, and root-cause analysis experience  Required Technical Skills  Advanced SQL, Python, and PySpark skills  Exposure to designing, developing, and maintaining automated data quality frameworks   Strong expertise in financial data testing, data profiling and root cause analysis   Exposure to cloud-based data platforms and services (AWS preferred)