QA Automation Engineer

Rakuten Rewards•San Mateo, CA

10d•$96,443 - $163,680•Hybrid

About The Position

Rakuten Rewards is seeking a hands-on QA Automation Engineer to join its Productivity Engineering organization. This individual contributor role focuses on ensuring the quality of backend services, APIs, event-driven systems, and data pipelines. The engineer will build reusable validation capabilities and AI-powered tools to improve testing efficiency, release confidence, and early defect detection, working with developers, QA, product, and DevOps throughout the development lifecycle. The role is onsite four days per week in Rakuten’s San Mateo, California office, with the remaining day onsite or offsite at the employee’s choice. The position is not eligible for visa sponsorship; applicants must be authorized to work in the United States without sponsorship or visa transfer by the employment start date. The expected base salary is $96,442.50–$163,680.00 annually. Eligible employees may receive a discretionary bonus and benefits including health, vision, and dental insurance, 401(k) matching, PTO, Volunteer Time Off, and other benefits as implemented.

Requirements

3 to 5 years of experience in Quality Assurance or Quality Engineering, including at least 2 years of hands-on backend or API test automation.
Hands-on software development experience in Java or a similar programming language, building test frameworks, automation services, AI tooling, or developer productivity solutions.
Experience applying LLMs or AI coding tools to testing and development workflows, such as generating tests, analyzing failures, or supporting defect triage; ability to review and validate test outputs.
Experience with REST APIs and backend services, complex SQL queries, and validating data integrity and business rules in PostgreSQL.
Understanding of event-driven systems and experience testing message queues or streaming workflows such as Kafka, including asynchronous behavior and error handling.
Experience with test frameworks such as pytest, JUnit, TestNG, or Spock.
Experience with GitHub workflows, including pull requests, code reviews, branching, release management, and CI/CD pipelines.
Knowledge of DevOps practices, AWS services, Docker, and Kubernetes, including deployment validation and troubleshooting test environment issues.
Analytical and communication skills, with the ability to independently own testing activities and collaborate across teams.
Ability to independently investigate production issues using AI-assisted troubleshooting tools such as Bits AI or similar solutions to support evidence-based root cause analysis.
Bachelor’s or master’s degree.

Nice To Haves

ISTQB Foundation Level Certification or equivalent.

Responsibilities

Own end-to-end quality for assigned backend features by defining test strategies, expanding coverage with AI tools, maintaining continuous validation, and partnering with engineering and product on requirements and release readiness.
Analyze requirements, service behavior, and production signals to identify edge cases, coverage gaps, missing telemetry, flaky tests, and configuration drift.
Build and maintain API, integration, and end-to-end tests in Java or a comparable language, using AI coding assistants and test agents to accelerate development, broaden coverage, and reduce maintenance.
Deploy and validate backend services in Docker and Kubernetes, and integrate automated tests and AI-assisted checks into GitHub release workflows to provide actionable feedback for changes.
Develop AI agents to exercise API workflows, generate test data, conduct exploratory backend testing, and validate interactions across services and databases, including business rules, API contracts, and data consistency across environments.
Validate microservices, event-driven workflows, message queues, and asynchronous pipelines, including retries, duplicate events, and failure recovery.
Use SQL and AI-assisted analysis to detect data anomalies, and run load tests to assess latency, throughput, and scalability.
Investigate production issues using logs, metrics, traces, and tools such as Bits AI; group related failures, support defect triage, and drive root cause analysis.
Validate findings, communicate release risk clearly, and promote effective AI-assisted quality practices across engineering teams.