QA Automation Engineer

About the role

Morningstar is looking for a QA Automation Engineer to create and sustain large‑scale automated data‑quality frameworks that validate financial datasets across multiple asset classes. The role involves collaborating with data engineering teams, ensuring data completeness, accuracy, and consistency for downstream investment and research workflows.

Responsibilities

• Design, develop, and maintain enterprise‑level automated data‑quality frameworks such as Great Expectations, AWS Glue Data Quality, or similar tools.
• Implement quality metrics and controls for multi‑asset‑class financial datasets.
• Build automated monitoring to detect anomalies, outliers, missing or stale data, and reconciliation issues.
• Validate investment data sourced from providers like Bloomberg, FactSet, LSEG, and others.
• Create source‑to‑target reconciliation pipelines across ingestion, transformation, and reporting layers.
• Partner with Data Engineering to embed quality checks throughout ETL/ELT processes.
• Support onboarding of new datasets, ensuring completeness, accuracy, and fitness for downstream use.

Eligibility

• Bachelor's or master's degree in quantitative, financial, economic, or engineering disciplines.
• 2–3 years of experience in data quality engineering, data engineering, analytics engineering, or related fields.
• Strong analytical mindset with experience in data profiling, anomaly detection, and root‑cause analysis.

Skills

• Advanced Python development.
• Proficient SQL querying and data analysis.
• Hands‑on experience with PySpark and distributed data processing.
• Building automated data validation and reconciliation frameworks.
• Familiarity with cloud data platforms, preferably AWS.
• Exposure to AI productivity tools such as ChatGPT, Microsoft Copilot, and GitHub Copilot.

Benefits