Senior Software Development Engineer in Test

FinWise Bank is seeking a Senior Software Development Engineer in Test to establish and scale quality engineering practices across the FinTech Connect API, the Nexus API, and the Tallied Credit Card Application. This role will design and implement the automation frameworks, CI/CD quality gates, test planning practices, release-readiness reporting, and AI-assisted testing capabilities needed to support reliable delivery across critical financial technology platforms.

The ideal candidate is a hands-on technical quality engineer who thinks like a developer, understands complex API and integration ecosystems, and can build durable test infrastructure rather than relying primarily on manual validation. This person will partner closely with software engineering, DevOps, product, architecture, and business stakeholders to improve release confidence, reduce production risk, and accelerate delivery.

Key Responsibilities

• Design, build, and maintain reusable API, integration, regression, smoke, contract, and end-to-end test automation frameworks for FinTech Connect, Nexus, and Tallied.
• Define and implement CI/CD quality gates for pull requests, merges, release candidates, and production readiness.
• Create formal test plans for major releases, partner integrations, regulatory-impacting workflows, and credit card application changes.
• Develop automated tests for REST APIs, backend services, integration workflows, data validation, authorization logic, account servicing, transaction flows, and partner-facing interfaces.
• Establish contract testing, mocking, stubbing, and service virtualization practices for external providers, partner integrations, and dependent systems.
• Implement load, performance, resiliency, negative-path, and failure-mode testing for high-volume API and credit card workflows.
• Use AI-assisted tooling to generate test scenarios, expand edge-case coverage, accelerate regression suite creation, and identify gaps in requirements or acceptance criteria.
• Use AI-assisted analysis to validate automated test results, summarize failures, detect anomalies, and distinguish product defects from test data, environment, dependency, or automation issues.
• Build release-readiness dashboards and quality metrics covering regression health, defect trends, coverage, performance, test reliability, and release risk.
• Partner with developers, product owners, DevOps engineers, and architects to define testable requirements, improve observability, and shift quality practices earlier in the delivery lifecycle.
• Support after-hours post-release smoke testing for critical production workflows when needed.
• Mentor QA engineers, software engineers, and delivery teams on automation standards, testability, AI-enabled quality practices, and quality-first engineering habits.