QA Automation Engineer (Data)

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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