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 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.