QA Automation Engineer

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 ourexpertisein asset allocation, investmentselectionand portfolio construction to create world-class investment strategiesleveragingthe full resources of Morningstar. The group specializes in multi-asset investing, using building blocks in equities, fixedincomeand 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: 

TheData Quality Automation Engineer will join theSystematicStrategiesteamin 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) 

• Exposure to AI productivity tools such as ChatGPT, Microsoft Copilot, and GitHub Copilot, with responsible validation of AI-generated outputs.
   

Preferred Qualifications 

• Bachelor's or master's degree in quantitative, financial, economic, or engineering discipline 

• 2+ years of experience in Data Quality Engineering, Data Engineering, Analytics Engineering, Financial Data Management, or Investment Technology 

Morningstar is an equal opportunity employer.