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.
