Staff Software Development Engineer in Test (Staff SDET)

Job Tittle - Staff Software Development Engineer in Test (Staff SDET)

Responsibilities

• Designing and evolving test automation frameworks and infrastructure to be robust, production-grade, and scalable with a strong emphasis on engineering well-structured, maintainable automation systems that raise the quality bar across the platform.
• Leading the adoption of AI-assisted testing practices and AI-enabled quality engineering capabilities, identifying where AI creates meaningful leverage in test generation, flakiness detection, coverage analysis, and release confidence and connecting those capabilities directly to engineering velocity, product quality, and measurable business outcomes.
• Defining and guiding test strategy and quality architecture across multiple teams, ensuring scalable and maintainable approaches to automated testing.
• Integrating shift-left testing practices throughout the software development lifecycle, ensuring quality is considered early and continuously throughout development.
• Establishing and owning quality metrics and measurement frameworks, including test coverage, defect leakage, flakiness, and test effectiveness, and using those signals to drive engineering decisions.
• Defining and advancing test data strategies, including synthetic data generation and data management for secure and scalable environments.
• Influencing system design and architecture to improve testability, reliability, and observability across the platform.
• Enhancing CI/CD pipelines by integrating automated testing and quality gates to enable continuous delivery and release confidence.
• Leading the strategy for reducing test infrastructure debt and modernizing legacy automation systems, including defining re-platforming approaches and driving execution across teams.
• Leading incident analysis and postmortems to identify quality gaps and drive systemic improvements.
• Guiding teams in selecting appropriate testing strategies across unit, integration, and end-to-end layers.
• Advancing adoption of modern testing approaches, including performance, security, accessibility, and AI-assisted testing techniques.
• Developing tooling and platform improvements to enhance test automation, developer productivity, and engineering efficiency across teams.
• Providing technical leadership and mentorship to SDETs and engineers, promoting best practices in test automation and quality engineering.
• Contributing to code reviews and technical discussions to ensure testability, maintainability, and high-quality standards.
• Promoting a culture of quality by advocating for best practices in testing, code quality, and release readiness across the organization.

Requirements

• S. in Computer Science, Software Engineering, or a related field, or equivalent practical experience.
• 7+ years of experience in software development, test automation, or quality engineering, with a demonstrated track record of defining test strategy and quality architecture across teams.
• Strong programming skills and proven experience building production-grade automated testing frameworks and tools.
• Strong computer science fundamentals, including data structures, algorithms, and industry-standard design patterns and practices as applied to test automation and quality systems.
• Deep understanding of testing methodologies, including unit, integration, end-to-end, performance, and security testing, with the ability to guide teams in selecting and applying the right strategies.
• Strong hands-on experience with cloud-based architectures and deploying, operating, and testing production applications on public cloud platforms (AWS, GCP, or Azure), including practical knowledge of cloud-native services and their implications for test strategy and environment management.
• Experience working with distributed systems and a strong understanding of how system design decisions impact testability, reliability, and observability.
• Experience integrating automated testing into CI/CD pipelines and modern software delivery workflows, including defining quality gates and driving continuous delivery practices.
• Experience defining and analyzing quality metrics to measure engineering effectiveness and drive data-informed decisions.
• A forward-looking approach to AI-assisted quality engineering: able to lead the adoption of AI tooling across test automation workflows, evaluate where AI creates meaningful leverage in testing and quality analysis, and connect those capabilities directly to release confidence, product quality, and business outcomes.
• Proven ability to influence engineers and stakeholders across teams to adopt quality best practices without direct authority.
• Strong debugging and root cause analysis skills, particularly in complex distributed systems.
• Strong communication skills with the ability to collaborate across engineering, product, and design teams and convey quality strategy to both technical and non-technical stakeholders.
• Experience working in Agile and DevOps environments with a strong understanding of modern software delivery practices.

Technology & Tools

• Proficiency in one or more of the following programming languages: Rust, C#, and JavaScript.
• Experience with test automation frameworks for API, UI, and service-level testing (e.g., Playwright, Selenium, or equivalent).
• Experience with performance and load testing tools (e.g., k6, Gatling, JMeter, or equivalent).
• Experience integrating automated testing into CI/CD pipelines and continuous delivery workflows, including automated testing gates and progressive release strategies.
• Experience with containerization and container orchestration (e.g., Docker, Kubernetes) as applied to test environment provisioning and scalable test execution.
• Experience with cloud-native infrastructure patterns, including infrastructure-as-code tooling (e.g., Terraform, Pulumi, or Bicep) for managing test environments and data infrastructure.
• Proficient with modern authentication and authorization mechanisms (e.g., OAuth 2.0, OIDC, SAML) and their implications for testing authenticated systems and services.
• Experience leveraging observability and monitoring tools to support testing, failure analysis, and system reliability (e.g., Open Telemetry, Datadog, Grafana, or equivalent).
• Familiarity with relational and non-relational data stores and the considerations that inform test data strategy, synthetic data generation, and environment isolation.
• Experience with modern security testing practices, including vulnerability scanning integration, dependency analysis, and security regression testing within delivery pipelines.
• Broad and deep knowledge of architectural styles and design patterns, with the ability to evaluate and influence system design for testability and reliability.