Lead QA Engineer

- Define and own the end-to-end test strategy for connector and platform releases — test planning, risk assessment, coverage targets, and entry/exit criteria.

- Act as the quality voice in sprint planning, design reviews, and release readiness; make clear go/no-go recommendations backed by data.

- Establish QA processes that scale: defect triage, severity/priority standards, regression tiers, test case management, and root-cause analysis for escaped defects.

- Track and report quality metrics — defect leakage, automation coverage, flaky-test rate, mean time to detect, release cycle time — to engineering and product leadership.

- Partner closely with development, product, support, and sustenance teams to shift quality left and reduce customer-reported issues.

Test Automation (Hands-On)

- Architect and evolve automation frameworks across React/TypeScript front ends, Java/Spring Boot microservices, REST/GraphQL APIs, and Kafka-based pipelines.

- Build resilient UI automation with Playwright or Selenium 4, including parallel execution and reliable test data management.

- Own API and contract testing (REST Assured, Pact, Postman/Newman) covering OAuth2, JWT, token, and Basic auth flows, pagination, throttling, and vendor error handling.

- Validate data end-to-end: ingestion → normalization → PostgreSQL persistence → aggregation → UI, including schema and migration regression.

- Build mock vendor APIs and record-replay harnesses so connector tests run without live third-party credentials.

- Own CI/CD quality gates in Jenkins or GitHub Actions with fast PR-level feedback and tiered regression runs.

AI-Driven & Self-Healing Automation

- Lead the design and rollout of self-healing automation: automatic locator recovery, DOM-similarity scoring, element fingerprinting, and confidence-based fallback that keeps suites green through UI changes.

- Apply LLMs across the QA lifecycle — generating test cases from requirements and API specs, converting manual tests to automated scripts, summarizing failures, and suggesting root causes from logs.

- Build intelligent flaky-test detection, failure classification, and auto-quarantine to separate real regressions from environmental noise.

- Introduce risk-based and change-impact test selection so CI runs the tests most relevant to each change.

- Evaluate AI testing tools (Healenium, Applitools, mabl, Testim, or in-house builds) and decide build-vs-buy based on accuracy, cost, and maintainability.

- Set guardrails for AI use in QA — validating AI-generated tests, measuring healing accuracy, and preventing false passes.

Qualifications

Experience

- 5–8 years in QA / test engineering, with at least 2 years leading or mentoring a QA team.

- Track record of building automation frameworks from scratch and owning release quality for a SaaS product.

- Hands-on experience building, extending, or productionizing an **AI-based or self-healing automation** solution — and the ability to explain how you measured its impact.

Skills

**Automation & Testing**

- Playwright and/or Selenium 4; TestNG or JUnit 5; Cypress, Jest, or Vitest

- API automation: REST Assured, Postman/Newman, GraphQL testing, schema validation

- Contract testing (Pact), service virtualization, and mocking (WireMock or similar)

- Performance testing with JMeter, k6, or Gatling

- Visual regression and accessibility testing

**Programming & Backend**

- Strong **Java** (17+) and **JavaScript/TypeScript**; Python is a plus

- SQL and data validation on PostgreSQL or similar

- Familiarity with microservices, Spring Boot, and Kafka or other event streaming systems

**AI & Machine Learning in QA**

- Self-healing locator strategies and ML-based element matching

- LLM integration: prompt design, structured outputs, and embedding model calls into test tooling

- AI-assisted test generation, failure clustering, and log/trace summarization

- Familiarity with RAG or vector search over test artifacts is a plus

- Working knowledge of AI testing platforms (Healenium, Applitools, mabl, Testim)

**DevOps & Tooling**

- CI/CD with Jenkins or GitHub Actions

- Docker and Kubernetes fundamentals

- Test management (Jira/Xray, TestRail, or Zephyr)

- Observability tools (Grafana, Prometheus, ELK) for test and pipeline diagnostics

**Leadership**

- Test strategy and planning for complex, multi-component releases

- People leadership: hiring, mentoring, performance feedback

- Clear communication with engineering, product, and leadership stakeholders

- Data-driven decision-making on quality and release readiness

Preferred

- Background in cybersecurity — vulnerability management, CSPM, EDR, SIEM, or identity.

- Experience testing multi-tenant SaaS at large data volumes.

- ISTQB Advanced / Test Manager certification.

- Open-source contributions to testing or automation projects.