QA Automation Engineer (Data)

Morningstar; Mumbai; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Morningstar Mumbai invites an experienced Data Quality Automation Engineer to join the Systematic Strategies team. You will design and implement automated data quality controls, validation frameworks, and reconciliation processes across large-scale data platforms to improve data quality and operational efficiency.

You will work with data engineers to embed controls in ETL/ELT pipelines, validate datasets from multiple providers, and ensure data completeness, accuracy, and fitness for downstream

Qualifications

• Advanced Python development experience.
• Strong SQL and data analysis skills.
• Hands-on PySpark and distributed data processing.
• Experience building automated data validation and reconciliation frameworks.
• Experience with large-scale structured and semi-structured financial datasets.
• Data profiling, anomaly detection, and root-cause analysis experience.

Responsibilities

• Design, build, and maintain enterprise-scale automated data quality frameworks like Great Expectations, AWS Glue Data Quality, Amazon Deequ, or similar data validation frameworks.
• Establish quality metrics for multi-asset class financial datasets.
• Develop automated monitoring controls to identify anomalies, outliers, missing data, stale data, and reconciliation breaks.
• Validate investment datasets from providers including Bloomberg, FactSet, Morningstar, LSEG, and others.
• Develop automated source-to-target reconciliation across ingestion, transformation, and reporting layers.
• Collaborate with Data Engineering to embed data quality controls in ETL/ELT pipelines.
• Support onboarding and robustness checks for new datasets, ensuring completeness and accuracy.

Skills

Advanced Python

SQL

PySpark

Data quality frameworks

Data profiling

Root-cause analysis

AI tools exposure

Education

Bachelor's or master's in quantitative/engineering domain

Financial data management

Tools

Great Expectations

AWS Glue Data Quality

Amazon Deequ

Cloud platforms (AWS)

Job description

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.