Senior Software Development Engineer In Test Jobs in Blue Bell, PA

We are seeking two hands-on Software Development Engineers in Test (SDETs) who treat AI as a core part of their workflow — not a convenience. You will be embedded on delivery teams executing key platform initiatives within a high-stakes, high-velocity delivery program, writing automated tests from day one.

The defining expectation for these roles: you will use GitHub Copilot and Claude Code to build CLI-driven test tools, scaffold entire Karate feature file suites, and generate Playwright test scripts at a speed that a purely manual approach cannot match. We want engineers who can sit down with a Copilot chat session, a service's OpenAPI spec, and emerge 30 minutes later with a working, parameterized Karate scenario suite.

What You'll Do:

• Author and maintain API and integration tests using the Karate framework (Karate DSL, JUnit5, Maven Surefire, Cucumber, JDBC for Oracle DB assertions) against Java/Spring Boot microservices
• Build Playwright + Python UI automation scripts for web-facing components; use Claude Code to generate page object models and test fixtures from design specs or running UIs
• Build bespoke CLI test tools in Python — driven by AI pair programming — that can hit service endpoints, validate responses, seed test data, or parse logs from the command line without needing a full test harness spun up
• Integrate all test suites into Jenkins CI/CD pipelines: write and own the pipeline stages, fix flaky tests, and keep quality gates green across dev, QA, perf, and e2e environments

Use AI tooling daily to:

• generate Karate .feature files from JIRA acceptance criteria, write Python test utilities from a prompt and a curl example, and debug pipeline failures by feeding stack traces to Claude Code for root-cause acceleration
• generate and manage synthetic test data (XML claim payloads, JSON request bodies, Oracle seed scripts) — using AI to extrapolate realistic variant coverage from known examples
• write contract tests that validate service API boundaries across independently deployed microservices on Kubernetes/OpenShift
• partner with engineers during feature development to define testability requirements, shift coverage left, and prevent regressions before merge

What We Expect from Your AI Usage This is not a "familiarity with Copilot" checkbox. We expect:

• You can prompt GitHub Copilot or Claude Code to generate a full Karate feature file suite from an OpenAPI spec in under an hour
• You can build a working Python CLI tool from scratch using Claude Code in a single pairing session — something that calls a REST endpoint, parses the response, and outputs a pass/fail summary
• You use AI to accelerate debugging: pasting Jenkins log output or a failed test stack trace into Claude Code and iterating to a fix, not waiting for someone else to triage it
• You review and refine AI-generated test code — you are the quality bar, not the generator.

Required Skills & Experience:

• 3+ years of hands-on SDET or automation engineering experience on production systems
• Proficiency in the Karate framework: DSL feature files, JUnit5 runner configuration, Maven Surefire integration, Cucumber reporting, and JDBC-backed DB assertions
• Playwright experience with Python for UI automation — locator strategy, async test patterns, CI integration
• Strong Python scripting skills: building CLI tools, calling REST APIs with requests or HTTPs, parsing JSON/XML, writing reusable test utilities
• Jenkins CI/CD: writing and debugging pipeline stages, not just running them
• Familiarity with Docker and Kubernetes/OpenShift for understanding service topology and test targeting
• Active, practiced daily user of GitHub
• Copilot and Claude Code — able to demonstrate concrete examples of test code you built with AI that you would not have written as fast by hand
• Comfortable in Bitbucket-based Git workflows with PR-based collaboration

Nice to Have Experience:

• with Oracle/SQL — writing JDBC assertion queries for claim data validation
• Familiarity with Spring Boot test slices (@SpringBootTest) for white-box unit/integration test contribution
• Exposure to Dynatrace or Kibana for correlating test failures to observability signals
• Familiarity with Helm or GitOps-based deployment models for understanding what changed between test runs
• Experience with RabbitMQ or message-driven integration testing