- Define and implement an end-to-end test automation strategy for a complex metadata-driven
platform
- Build automated test coverage across:
o API layers o platform runtime behavior
o metadata interpretation and rendering
o multi-tenant routing and execution context
o site vs workspace behaviors o authentication, authorization, and session flows
o dynamic UI rendering and interaction o AI-assisted workflows and platform integrations
- Design regression suites that validate both expected behavior and known high-risk platform areas
- Create adversarial, exploratory, and negative-path automation intended to expose hidden bugs and
edge-case failures
- Develop tests that validate the correctness of context resolution from URL structure and request
parameters
- Verify that the platform correctly handles tenant isolation and boundary conditions
- Build test coverage for metadata-defined views, including malformed, incomplete, conflicting, and
unexpected YAML configurations
- Validate AI-related user flows, including invocation of agents, tool-driven actions, generated
updates, and application of results into the platform
- Verify that AI-powered capabilities behave correctly across tenant, site, and workspace boundaries
- Validate that changes in backend services do not introduce regressions in platform execution, data
loading, rendering behavior, or AI-assisted workflows
- Create automated validation for site and workspace execution differences
- Build a maintainable framework for:
o API testing
o UI/browser testing o integration testing
o contract testing o negative testing
o concurrency and resilience testing
- Partner with engineering leadership, backend engineers, frontend engineers, and AI engineers to
identify critical risk areas and define release gates
- Integrate automated tests into CI/CD pipelines so regressions are detected early
- Help establish quality standards, test data strategies, environment strategies, and defect triage
practices
- Produce actionable reporting on coverage, quality trends, flaky tests, and platform risk What We
Need We need someone who knows how to test systems where:
- behavior is generated dynamically from metadata
- UI structure is not static – request execution depends on runtime context
- multiple tenant and application boundaries exist
- AI capabilities are embedded into user-facing platform workflows
- defects often live in interaction effects between layers, not just in isolated functions You should be
comfortable working in ambiguity, digging through complex behavior, and building test systems that
are both strategic and practical.
