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.
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.
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