QA Automation Engineer (Data)

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

Benefits offered by this job

Hybrid work model

Benefits package

Job summary

Morningstar is seeking a Data Quality Automation Engineer to join the Systematic Strategies team. You will design and implement automated data quality controls, validation, reconciliation, and monitoring across enterprise data platforms.

Ideal candidates will have advanced Python, strong SQL, PySpark experience, and familiarity with financial datasets from major data vendors. Hybrid work with global collaboration is offered.

Qualifications

• Advanced Python development experience.
• Strong SQL and data analysis skills.
• Hands-on PySpark and distributed data processing experience.
• Experience building automated data validation and reconciliation frameworks.
• Experience with large-scale structured and semi-structured financial data.

Responsibilities

• Design, build, and maintain automated data quality frameworks (e.g., Great Expectations, AWS Glue Data Quality, Deequ).
• Establish quality metrics for multi-asset class financial datasets.
• Develop monitoring controls for anomalies, outliers, and data gaps.
• Validate datasets sourced from vendors like Bloomberg, FactSet, Morningstar, LSEG.
• Create source-to-target reconciliation across ingestion, transformation and reporting layers.
• Collaborate with Data Engineering to embed data quality in ETL/ELT pipelines.
• Support onboarding and robustness checks for new datasets.

Skills

Advanced Python

SQL

PySpark

Data quality frameworks

Data profiling

Education

Bachelor’s or Master’s in quantitative/financial/engineering

Tools

Great Expectations

AWS Glue Data Quality

Amazon Deequ

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 theSystematicStrategiesteam 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

• 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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