Software Engineer: Test (SDET)

Remote / Chicago, IL (Hybrid)

About the Role

As a Software Engineer: Test at LogicGate, you will build and scale the automated testing frameworks, pipelines, and tools that guarantee the reliability of the Risk Cloud platform. AI is central to how you work: you are expected to embed AI-driven tooling across test authoring, regression analysis, and coverage expansion to compress delivery timelines and raise the precision of everything you ship.  You will design robust automation suites, expand test coverage across the ecosystem, and systematically isolate complex software regressions. This role is ideal for an engineering-focused QA professional who treats AI as a core part of their craft, takes operational ownership of quality boundaries, and establishes data-driven testing strategies across our platform.

What You’ll Do

• Automation Engineering: Design, build, and maintain scalable automated testing frameworks and end-to-end regression suites using TypeScript and Playwright, augmented by AI-driven code generation, to take validation tasks from requirements through continuous deployment.
• Testing Pyramid Validation: Write clean, understandable automation code to validate backend APIs and integration layers, strictly adhering to the testing pyramid to ensure a highly stable and dependable deployment lifecycle.
• Swarm Testing & Edge Cases: Actively contribute to and expand team testing efforts, systematically validating edge cases, error conditions, and happy paths while guiding the broader Engineering Department and QA Analysts on automated testability best practices.
• Systematic Debugging: Independently diagnose and isolate test automation failures and platform defects by reading distributed system logs, analyzing API console data, and utilizing AI-powered Datadog monitoring patterns to accelerate root-cause resolution.
• Technical Documentation: Author clear technical documentation within the codebase and Confluence, including test strategies, automation runbooks, and detailed defect reports to support collective engineering knowledge sharing.
• Test Data Infrastructure: Work operationally with relational databases (PostgreSQL) and advanced platform data stores, utilizing AI tools to query, manage, and optimize automated test data flows.
• Agile Collaboration: Partner cross-functionally with product managers, feature developers, QA Analysts, and DevOps in Agile sprints, accurately estimating validation effort, mapping task prioritization, and raising project dependencies or blockers daily.
• Code Reviews & Quality Culture: Participate actively in regular peer code reviews for both application features and test suites—providing constructive design feedback and ensuring strict compliance with team quality conventions.
• Testing Standards & Strategy: Partner with Engineering Leadership to help define department-wide QA expectations — incorporating AI governance, coverage ownership, automated-vs-manual boundaries, and documentation standards like runbooks — and translate that direction into practices the framework and broader QA function can follow.
• QA Analyst Enablement:  Partner directly with QA Analysts across squads to build their automation skills; pair on writing tests, review their automation code, and help shift their time from manual regression toward automated coverage.
• Testing Standards & Strategy: Partner with Engineering Leadership to help define department-wide QA expectations — coverage ownership, automated-vs-manual boundaries, and documentation standards like runbooks — and translate that direction into practices the framework and broader QA function can follow.

What You Bring

Required

• Professional Experience: 3–5 years of professional experience in software test automation, systems validation, or backend engineering within a high-growth SaaS environment.
• Core Language Proficiency: Strong programming experience using TypeScript or a comparable scripting language, alongside practical exposure to automated testing frameworks.
• AI-Augmented Development: Practical experience using AI coding assistants (Cursor, GitHub Copilot, Codex, or Claude Code) as part of a normal workflow—writing effective prompts, building reusable skills or custom instructions, and knowing when to trust, verify, or discard AI-generated output.
• Testing Strategy Mastery: Deep command of Fowler's Testing Pyramid and the ability to apply it pragmatically - knowing what belongs at the unit, integration, and end-to-end layers and why - complemented by hands-on proficiency writing API tests, functional end-to-end tests, and performance tests to validate quality at every level of the stack.
• Mentoring and Coaching: Experience coaching or upskilling less-automation-savvy testers or engineers — you multiply impact through others' skills, not just your own code.
• Autonomous Troubleshooting: Demonstrated ability to independently debug distributed backend microservices by parsing logs and utilizing core enterprise monitoring platforms like Datadog.

Nice to Have

• Advanced Data Infrastructure: Familiarity with validating message queues (RabbitMQ), graph databases (Neo4j), or distributed caching layers (Redis) under automated scale conditions.
• SaaS Security & Architecture: Background validating multi-tenant B2B SaaS applications where strict data isolation, high availability, and security compliance verification tools (like GitLab Ultimate, Sonarqube and Wiz) are paramount.
• Reporting and Presenting: Experience building or maintaining lightweight reporting/dashboards that surface test coverage (automated vs. manual) across teams.