QA Automation Engineer

The Quality Assurance Engineer will play a critical role in ensuring the accuracy, reliability, and readiness of the firm’s modernized Investment Calculation Services. This position combines deep data quality analysis, automation strategy, and domain knowledge development to support the transformation of core calculation and data platforms.

Key Responsibilities

• Data Quality Validation & Root Cause Analysis:
  • Utilize internal comparison tools to evaluate data quality—accuracy, completeness, consistency, coverage, and timeliness—across multiple storage systems, including:
  • TSDB 1.0 (file-based) vs. PostgreSQL
  • On‑premises MS SQL vs. AWS‑hosted MS SQL
  • Investigate and analyze data discrepancies by tracing upstream sources (e.g., XII, Document Warehouse) to identify and verify root causes.
  • Develop scripts or Python-based utilities to support ongoing data quality assessment and reporting.
• Domain Knowledge & Insights:
  • Leverage internal AI knowledge bases to understand complex business data flows, underlying business logic (including logic reverse‑engineered from code), data dependencies, data mappings, data lineage, and the broader investment‑calculation domain.
  • Apply domain insights to enhance testing completeness and ensure alignment with business logic and system behaviors.
• Test Automation Strategy & Execution:
  • Define, establish, and refine the automation strategy for modernization initiatives.
  • Design and implement automation frameworks that support the application’s complex and distributed architecture.
  • Translate business and technical requirements into comprehensive, maintainable automated test cases.
  • Own the QA lifecycle for assigned components, providing testing guidance and best practices to engineering teams.

Qualifications:

• A bachelor's in Computer Science or related.

• At least 1-3 years of experience in Python in a commercial application or commercial service environment.
• Minimum 2 years of experience working with SQL and relational databases.
• Experience analyzing and validating large-scale datasets.
• Experience in AWS cloud first architecture like Lambdas, ECS, EC2, Fargate, S3, RDS DB, API gateway, Serverless, Redis Cache, SQS, ASG/LB/TG, CloudWatch and Code Pipeline.

• Familiarity with secure coding practices.

• Experience with Agile methodology and tools like JIRA.

• Be organized and able to remain productive even when you have multiple deliveries.

Nice to have:

Familiarity with the financial services domain (accounts, portfolios, holdings, returns, performance streams, traded instruments, etc.)

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.