QA Automation Engineer (Data)

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

Benefits offered by this job

Hybrid work model

In-office four days a week

Flexible benefits

Job summary

Morningstar India Private Ltd. is seeking a Data Quality Automation Engineer to join the Systematic Strategies team in the Research & Investment group.

This role designs and implements automated data quality controls across enterprise-scale data platforms, enabling robust investment data workflows. The ideal candidate will have advanced Python, SQL, PySpark, and experience with data quality frameworks like Great Expectations, AWS Glue Data Quality, or Deequ, plus familiarity with large financial

Qualifications

• Advanced Python development and SQL/data analysis skills.
• Experience with PySpark and large-scale datasets in finance.
• Experience building automated data validation and reconciliation frameworks.
• Familiarity with data quality controls across ETL/ELT pipelines.
• Data profiling, anomaly detection, and root-cause analysis.

Responsibilities

• Design, build, and maintain enterprise-scale automated data quality frameworks.
• Establish quality metrics for multi-asset class financial data.
• Develop automated monitoring to identify anomalies and reconciliation breaks.
• Validate datasets from providers like Bloomberg, FactSet, Morningstar, LSEG.
• Create source-to-target reconciliation frameworks across ingestion to reporting.
• Collaborate with Data Engineering to embed data quality in pipelines.
• Support onboarding and robustness checks for new datasets.

Skills

Python

SQL

PySpark

Data quality frameworks

Financial data

Data profiling

Education

Bachelor's or Master's in quantitative/financial/econ/engineering

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 ourexpertisein asset allocation, investmentselectionand portfolio construction to create world-class investment strategiesleveragingthe full resources of Morningstar. The group specializes in multi-asset investing, using building blocks in equities, fixedincomeand 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: TheData Quality Automation Engineer will join theSystematicStrategiesteamin 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.

I10_MstarIndiaPvtLtd Morningstar India Private Ltd. (Delhi) Legal Entity

Morningstar is a global independent investment research and financial data company. Here, you'll help uncover what’s hidden, simplify what’s complex, and create insights that empower investor success.

Company overview Morningstar Development Program Morningstar is strongly committed to creating and preserving equal opportunity for all employees and applicants. We make all employment decisions—including recruitment, hiring, compensation, training, promotion, transfer, discipline, termination, and other personnel matters—without regard to race, color, ancestry, religion, sex, national origin, age, disability, protected veteran status, marital status, sexual orientation, genetic information, citizenship, gender identity and expression, parental status, or other legally protected characteristics or conduct.