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 expertisein asset allocation, investment selectionand portfolio construction to create world-class investment strategies leveragingthe full resources of Morningstar. The group specializes in multi-asset investing, using building blocks in equities, fixed incomeand 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 Engineerwill join the Systematic Strategies team in the Research & Investment group. This experiencedprofessionalwill design and implement automated data quality controls, validation frameworks, reconciliation processes,and monitoringsolutions across enterprise-scale data platforms.The ideal candidate combines expertisefinancial data managementand automation engineering, with a passion for building scalable controls that improve data qualityand operational efficiency.

Key Responsibilities

The successful candidate will

• Design, build, and maintainenterprise-scale automated data quality frameworkslikeGreat Expectations, AWS Glue Data Quality, Amazon Deequ, or similar data validation frameworks

• Experience implementing and maintainingdata quality controlsby establishingquality metricsfor multi-asset class financial datasets.

• Develop automated monitoring controls capable of identifyinganomalies, 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 PySparkand distributed data processing

• Experience building automated data validation and reconciliation frameworks

• Experience working with large-scale structured and semi-structureddatasetsin financial domain

• Data profiling, anomaly detection, and root-cause analysis experience

Required Technical Skills

• Advanced SQL, Python, and PySparkskills

• Exposure todesigning, developing, and maintainingautomated data quality frameworks

• Strong expertisein financial data testing, data profilingand 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 ofAI-generated outputs.

Preferred Qualifications

• Bachelor's or master'sdegree 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.

Morningstar's hybrid work environment gives you the opportunity to collaborate in-person each week as we've found that we're at our best when we're purposely together on a regular basis. In most of our locations, our hybrid work model is four days in-office each week. A range of other benefits are also available to enhance flexibility as needs change. No matter where you are, you'll have tools and resources to engage meaningfully with your global colleagues.