QA Engineer

burfordcap•New York, NY

5d•$100,000 - $120,000•Onsite

About The Position

Burford Capital is seeking a detail-oriented and technically capable QA Engineer to support the quality of complex, data-driven financial applications through a combination of manual testing, test planning, and automation engineering. The ideal candidate will have approximately five years of experience in software quality assurance, including hands-on experience designing test cases, performing exploratory and regression testing, and building maintainable automated test suites. The role will also use modern AI-assisted development tools to accelerate test creation, identify edge cases, support automation development, and improve failure analysis, while applying appropriate engineering judgment and validation. The successful candidate will work closely with developers, quantitative professionals, and business users to ensure that software and data solutions are reliable, accurate, performant, and production-ready. The QA Engineer will work as part of Burford’s Core Services group and contribute to a variety of software and data projects across the firm’s evolving tools and platforms. The individual will help define acceptance criteria and testing strategies, execute manual and automated tests, develop reusable testing tools, and integrate quality checks into the software development and delivery process. The role will collaborate with developers, business analysts, business stakeholders, and vendors to deliver solutions aligned with company goals and priorities. The ideal candidate will be an energetic, collaborative, and proactive individual who is comfortable working across multiple projects, sharing expertise with others, investigating issues in depth, and taking ownership of results.

Requirements

Approximately five years of professional experience in software quality assurance, quality engineering or software testing, including meaningful hands-on test automation experience.
Demonstrated ability to create test strategies and detailed test cases, perform manual and exploratory testing, and make sound risk-based decisions about what should be automated.
Proficiency in at least one programming or scripting language, preferably Python or TypeScript/JavaScript, with an ability to write readable, maintainable test code.
Hands-on experience with modern browser automation and API testing tools such as Playwright, Cypress, Selenium, PyTest, Postman, REST Assured or equivalent technologies.
Working knowledge of SQL and experience validating application data in relational or cloud data platforms.
Experience integrating automated tests with CI/CD pipelines and working with Git-based development workflows.
Understanding of smoke, functional, regression, integration, end-to-end and performance testing approaches, including the strengths and limitations of each layer.
Experience using AI-assisted development or testing tools to improve productivity, together with the judgment to validate generated tests and avoid exposing confidential or regulated information.
Strong analytical, debugging and communication skills, with the ability to collaborate across technical and nontechnical teams.

Nice To Haves

Experience testing financial, legal, quantitative or other data-intensive enterprise applications.
Familiarity with Azure, Docker, cloud-hosted applications and test execution in containerized environments.
Exposure to accessibility, performance, security or model-output validation testing.
Experience improving flaky test suites, test data management or cross-application end-to-end coverage.
Bachelor's degree in computer science, engineering or a related discipline, or equivalent practical experience.

Responsibilities

Test planning and design: Review business requirements, user stories and technical designs; define acceptance criteria, test plans, test cases and risk-based coverage for new features, fixes and releases.
Manual and exploratory testing: Execute functional, regression, integration, usability and exploratory testing across browser-based applications and complex business workflows.
Automation development: Build and maintain reliable automated UI, API and integration tests using tools such as Playwright, Cypress, Selenium, PyTest or equivalent frameworks.
AI-assisted quality engineering: Use approved generative AI and coding assistants to draft test scenarios, generate automation scaffolding, improve coverage, analyze failures and maintain test assets. Review generated output for correctness, security, maintainability and business relevance.
API and data validation: Test RESTful services, data pipelines and database integrations; use SQL to validate data accuracy, completeness and consistency across PostgreSQL, Snowflake, SQL Server or similar platforms.
CI/CD integration: Incorporate automated tests into Azure DevOps, GitHub Actions or comparable pipelines and help define appropriate quality gates for pull requests, deployments and scheduled regression runs.
Defect management: Record clear, reproducible defects; partner with engineers to isolate root causes; verify fixes and identify opportunities to prevent recurrence through automation or process improvement.
Test environments and data: Help create repeatable test data, fixtures, mocks and environment setup that support parallel development and dependable automated execution.
Quality reporting: Communicate test results, release risks, escaped defects, automation health and coverage gaps in a concise and actionable manner.
Shift-left collaboration: Participate early in the software development lifecycle, challenge unclear requirements and help teams design applications that are observable, testable and resilient.