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Archer

Quality Assurance Engineer

San Jose, California, United States$130k–$150kmidAdded today

About this role

Archer, an aerospace company developing electric vertical takeoff aircraft, seeks a Quality Assurance Engineer to establish a comprehensive QA function from scratch for their data-intensive visualization platform. You'll design test strategies, build automation across web, APIs, and data pipelines, and validate data correctness at scale using modern tools and GenAI.

What you'll do

  • Own full testing lifecycle including test planning, execution, bug triage, and release verification
  • Design and build automated test suites in Python and JavaScript using Playwright for frontend, backend, and data pipeline coverage
  • Validate end-to-end data correctness from ingestion through transformation to dashboards
  • Design and execute stress and performance testing for high-volume data and real-time charting
  • Establish test case management, CI/CD integration with GitHub Actions, and bug tracking processes
  • Partner with engineering, product, and data teams to define testable acceptance criteria

What they're looking for

  • Python and JavaScript test automation
  • Test automation frameworks and Playwright
  • SQL and relational database querying for data validation
  • CI/CD pipelines and GitHub Actions
  • Data pipeline and data-heavy application testing
  • Stress and performance testing methodologies
  • AWS, Kubernetes, and cloud-native infrastructure
  • Test strategy design and QA best practices
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Archer

Archer develops all-electric vertical takeoff and landing (eVTOL) aircraft for sustainable aviation. The company is hiring engineers across electrical integration, software, aerodynamics, and controls to build and validate autonomous flight systems, propulsion architectures, and aircraft design.

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Likely interview questions

  • Tell us about a time you built QA processes or tooling from the ground up—what challenges did you face and how did you overcome them?
  • How have you approached validating data correctness in large-scale data pipelines, and what tools or techniques did you use?
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