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) 

• 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.