QA Automation Engineer (Data)

Overview

QA Automation Engineer (Data) for the Systematic Strategies team in Morningstar’s Research & Investment group.

What you'll do

• Design, build, and maintain automated enterprise data quality frameworks (e.g., Great Expectations, AWS Glue Data Quality, Amazon Deequ).
• Implement and maintain data quality controls using quality metrics for multi-asset class financial datasets.
• Develop monitoring controls to detect anomalies, outliers, missing data, stale data, and reconciliation breaks.
• Validate investment datasets from external providers including Bloomberg, FactSet, Morningstar, and LSEG.
• Build source-to-target reconciliation frameworks across ingestion, transformation, and reporting layers.
• Collaborate with Data Engineering teams to embed data quality controls in ETL/ELT pipelines.
• Support onboarding and robustness checks for new datasets (completeness, accuracy, consistency, fitness for downstream workflows).

What you'll need

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

Details

• Location: Mumbai.
• Hybrid work environment with four days in-office each week in most locations.