QA LEAD / AUTOMATED TEST ENGINEER

Key Responsibilities

• Automation Strategy & Frameworks: Design and implement scalable automated testing frameworks covering smoke, regression, integration, and performance testing across multiple financial models and systems.

• Quant Model Validation: Develop and maintain automated testing scripts for quantitative models, ensuring numerical consistency, reproducibility, and compliance with validation standards.

• Test Tooling & Scripting: Build, maintain, and optimize automated test suits using Python, PyTest, Selenium, Robot Framework, or equivalent tools. Integrate with CI/CD pipelines for continuous validation.

• Test Planning & Execution: Define comprehensive test plans, test cases, and coverage metrics for new features, bug fixes, and infrastructure updates.

• Collaboration with Development Teams: Partner closely with developers and modelers to understand changes, define acceptance criteria, and ensure automated tests are incorporated early in the SDLC.

• Continuous Integration & Cloud Testing: Develop automated regression pipelines integrated with Azure DevOps, AWS or similar CI/CD systems. Familiarity with Dockerized or cloud-native testing environments required.

• Data & API Testing: Validate large-scale data flows, API endpoints, and SQL/Postgres/Snowflake integrations to ensure data consistency and system reliability.

• Test Reporting & Quality Metrics: Produce clear, actionable test results and dashboards. Identify trends, gaps and improvement opportunities in release readiness.

• Governance & Best Practices: Establish testing standards, reusable frameworks, and version control for automated test assets. Promote a culture of continuous improvement and quality engineering excellence.

Required Skills & Experience

• Automation Tools: PyTest, Selenium, Robot Framework, Cypress, or equivalent.

• Scripting Languages: Advanced Python proficiency required; exposure to Bash/PowerShell a plus

• Databases: SQL proficiency with PostgreSQL, Snowflake, or SQL Server.

• Cloud Platforms: Experience testing in Azure or AWS environments

• CI/CD Pipelines: Hands-on experience with Azure DevOps, GitHub Actions, or Jenkins for automated test orchestration

• API Testing: Postman, REST Assured, or equivalent for validating RESTful endpoints

• Quant Model Testing: Familiarity with model output validation, Monte Carlo Simulation comparisons, and numerical precision testing

• Version Control: Git and branching workflows

• Testing Methodologies: Strong understanding of smoke, regression, functional, integration and performance testing strategies.

• Security & Compliance Testing: Exposure to validation practices in regulated financial environments.