Quality Assurance Engineer I, Leo Network Observability, Visibility & Analytics team

Amazon Leo is Amazon"s Low Earth Orbit satellite network, with a mission to deliver fast, reliable internet connectivity to customers beyond the reach of existing networks. From individual households to schools, hospitals, businesses, and government agencies, Amazon Leo will connect people and organizations operating in locations without reliable connectivity.

The Leo Network Observability, Visibility & Analytics team builds the systems that provide deep, end-to-end insight into the health, performance, and reliability of this global telecom network. Our systems span the full Leo service, from satellites and customer terminals to ground gateways, cloud services, and terrestrial network infrastructure, and help engineers understand network behavior, detect failures quickly, and diagnose complex issues.

We are looking for a Quality Assurance Engineer to help ensure the network health metrics, dashboards, and APIs our engineers and leaders rely on are accurate, reliable, and ready for release. You will test systems that turn network telemetry and events into answers to critical questions: Is the network healthy? Where is a failure occurring? What is its customer impact?

Export Control Requirement: Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum.

Key job responsibilities

• Create and execute test plans and test cases for new and existing features across our network health services, data pipelines, APIs, and dashboards, ensuring thorough functional, integration, and end-to-end coverage across straightforward and moderately complex scenarios.
• Develop, maintain, and extend automated tests for APIs, UI, and data pipelines using languages such as Python, and integrate them into CI/CD pipelines to reduce manual testing effort.
• Validate the accuracy of network health metrics (such as availability, latency, and packet loss) against source data and expected results.
• Troubleshoot and isolate defects from test and pipeline failures, file clear and actionable bug reports, and work with developers to drive root cause analysis and resolution.
• Review requirements, designs, and change requests with engineers and product managers to identify risks and test gaps early, and recommend changes that improve quality and the customer experience.
• Participate in defect triage, test planning, and prioritization discussions, helping the team balance time constraints with test coverage goals.
• Use AI tools to help generate test cases, expand coverage, and speed up defect investigation.
• Track quality metrics and share release readiness status with the team.

A day in the life

As a QAE on the Network Observability team, you"ll work with software engineers, network engineers, and product managers to review new features, identify risks, and plan how to test them. You might validate that a new availability metric matches the underlying telemetry one day, and build API automation for a new service the next. You"ll write and run automated tests, triage failures in CI/CD pipelines, and partner with developers to reproduce and fix defects. You"ll take part in sprint ceremonies and design reviews, and help make sure the data engineers and leaders use to operate the Leo network is right.

About the team

The Leo Network Observability, Visibility & Analytics team is responsible for making a highly distributed global telecom network understandable and operable at scale. We build the visibility layer that connects network signals across satellites, customer terminals, gateways, cloud services, and terrestrial infrastructure, turning individual telemetry signals into an end-to-end view of network health so issues can be detected, diagnosed, and resolved faster.

The problems we solve are inherently distributed and combine software engineering with networking and data science. We are building new capabilities rather than applying existing observability patterns to a conventional cloud service. If you enjoy ambiguous problems, care about getting the details right, and want to help build foundational systems for Amazon Leo, this team is a great place to grow.