Senior QA Automation Engineer

Juniper Square•,

2d•$119,000 - $149,000•Remote

About The Position

As a QA Engineer at Juniper Square, you will collaborate with the product and engineering teams to help define and drive our manual and automation testing efforts. You must be detail oriented, passionate about product quality, and a strong advocate for the end user experience. Juniper Square is a company that operates in the private markets sector, aiming to unlock their full potential through technology, data, and fund administration services. They have a strong mission, a growing team of over 1,000 employees, and are backed by significant funding. The company culture emphasizes ambitious and meaningful work, ownership, urgency, problem-solving, open debate, collaboration, and diverse perspectives. They operate with transparency and value feedback. Juniper Square offers flexible work arrangements, including fully remote options and physical offices in San Francisco, New York City, Mumbai, and Bangalore. Their teams collaborate across multiple countries and provinces.

Requirements

Bachelor's degree in Computer Science, or equivalent professional experience.
5+ years in Software Quality Assurance, with strong expertise in testing web applications.
Skilled in designing, building, and maintaining test automation frameworks for web applications (preferably using Playwright).
Proficiency in at least one programming language, such as JavaScript, TypeScript, or Python.
Experience in using AI-powered tools (e.g., Augment, Cursor, Claude) to accelerate test authoring, assist in debugging automation scripts, and optimize documentation workflows.
Hands-on experience in both manual, and/or automated testing of REST and/or GraphQL APIs using tools like Postman, RestAssured, or Locust.
Solid understanding of CI/CD pipelines and software development lifecycle concepts, including code review practices, code coverage analysis, continuous testing, and delivery.
Familiarity with version control systems (e.g., Git), test management tools (e.g., TestRail), and issue-tracking platforms (e.g., Jira).
Experience in creating comprehensive test plans, authoring test cases, executing tests, and adhering to QA best practices.
Excellent analytical and problem-solving abilities, attention to detail, and the ability to work independently within Agile development teams.
Strong written and verbal communication skills in English.

Nice To Haves

Working knowledge of relational databases and SQL is a plus.
Knowledge of cloud environments (e.g., AWS) and experience with application deployments.

Responsibilities

Review functional specifications and technical design documents, providing meaningful feedback and using AI-assisted analysis to identify edge cases or logic gaps early in the SDLC.
Work closely with development teams to identify test cases for automation and leverage AI-powered IDEs (like Cursor or Augment) to rapidly prototype and scaffold new test suites.
Design, develop, and execute test cases against product features and backend systems.
Maintain and extend automation frameworks (Playwright/Locust) by utilizing LLMs for code refactoring, optimizing test scripts, and reducing flaky test patterns.
Contribute to your development team on best practices, processes, and problem-solving, including the ethical and effective use of AI tools in the QA workflow.
Develop automated test result reports and highlight any potential quality risks.
Identify, troubleshoot, and track bugs to resolution, using AI tools to assist in root cause analysis and log interpretation.
Be an advocate for automation: Partner with the engineering team to provide recommendations on how to decrease manual efforts through automated solutions and AI-driven test generation.
Drive best practices for the ethical and effective use of AI tools within QA workflows and across the broader engineering team.
Contribute to testing strategies for AI-powered features, including validation of LLM outputs, edge cases, and reliability.
Experience designing evaluation frameworks for LLM-powered features, including prompt regression testing and behavioral drift detection.
Proactively leverage AI tools (e.g., Cursor, Claude, etc…) to accelerate test authoring, debugging, and maintenance of automation frameworks.
Use AI to diagnose failures, generate test scenarios, and improve coverage and efficiency.