Responsibilities:

Test Automation & Framework: Lead the development of automated test framework for financial data pipelines using PySpark/Python and AWS services (e.g., Glue, Athena, EMR, Redshift) Should be able to write test cases based on product requirement, execute them and report issues
Data Validation: Automate data validation for financial data sets (like back test datasets, prices and returns data and other calculated metrics), ensuring data consistency and correctness across distributed systems
Big Data Testing: Use PySpark / Python to test large financial datasets, validating performance, accuracy, and scalability in AWS environments
Collaboration: Work with data analysts, portfolio managers, and quant researchers to define test requirements and ensure data quality in financial data sets like Back test universe, Risk Models and reports
Continuous Improvement: Optimize test processes, creating reports and frameworks for efficiency, integrating new tools and best practices for cloud-based systems

Requirements:

Education:

Bachelor s or MBA/Masters degree in a quantitative, financial discipline, or engineering
3+ years of hands-on experience in QA automation with a focus on data-driven financial solutions, preferably in AWS environment

Technical Expertise:

Automation Frameworks & Tools:

Strong knowledge of a testing framework for API, and data-driven test automation
Hands-on experience with integrating test automation into Code Pipeline or cloud-based CI/CD systems to ensure consistent and reliable testing

Data Analysis & Financial Modeling Knowledge:

Proficiency in SQL and MS Excel and ability to work with large financial datasets, performing data validation for downstream usage of financial calculations and reports.

Performance Testing & Optimization:

Experience in performance testing and load testing for large financial data applications, optimizing AWS-based systems for scalability and reliability

Skill that would be a plus:

Strong proficiency in Python, with extensive experience using PySpark to process large datasets and validate financial data across distributed systems
Strong expertise in AWS services including S3, Glue, EMR to design and automate testing for financial data systems in the cloud
Basic understanding of financial data analysis workflows and key financial metrics, including portfolio performance, risk management, and pricing models
Experience with AWS CDK, CloudFormation, or Terraform for infrastructure automation, ensuring test environments are reproducible and scalable

Personal Attributes:

Excellent ability with a strong track record of fostering collaboration across multiple teams
Exceptional problem-solving skills, with the ability to troubleshoot complex data issues and identify root causes in large-scale cloud-based systems
Strong communication skills, with the ability to present technical information clearly to both technical and non-technical stakeholders
Highly motivated, pro-active, and passionate about continuous learning, keeping abreast with emerging trends in cloud technologies, data analysis, and automation

Morningstar is an equal opportunity employer.

Sr. QA Automation Engineer (Data)