Job Title: QA Automation Engineer

Location: Onsite- Middletown, NJ

Education: B.S. in Computer Science or related field

Overview

We are seeking a Senior QA Automation Engineer to design, implement, and operate a fully automated QA ecosystem covering test case design, automation, execution, and reporting. This role ensures source to target validation as systems transition from preoperatory architectures to client's target architecture, with all QA automation embedded into CI/CD pipelines. The engineer leverages Agentic AI to accelerate test creation, validation, and regression detection across APIs, data platforms, and frontend/backend services.

Daily Responsibilities:

• End to end QA automation ownership across UI, API, and data layers using Selenium / Playwright / Cypress and Python (pytest)
• Full CI/CD integration (Azure DevOps, GitHub Actions) with automated execution on PRs, merges, and scheduled regressions
• Strong focus on regression, negative testing, and pipeline gated quality enforcement
• REST API automation validating status codes, schemas, payloads, OAuth flows, and error scenarios
• Data automation using Snowflake (SQL, pyodbc, connectors) for source to target validation, reconciliation, and schema checks
• API contract and schema validation using JSON/schema frameworks and mocking for isolation
• Design of scalable test architectures (unit, integration, E2E, UI) with data driven and parameterized testing
• Deterministic test data management (fixtures, mocks, setup/teardown)
• Automated test execution reporting (Allure / pytest html / JUnit) integrated into CI dashboards

Required Qualifications:

• Strong Python and SQL skills for automation and validation
• GitHub automation and REST tooling; comfort with Postman, curl, JSON/JSONL
• Backend, frontend, and platform level QA across distributed systems
• Source to target validation comparing preoperatory architecture outputs to target architecture
• Validate functional parity, API consistency, and data accuracy, accounting for legacy vs target design differences
• Use Agentic AI to autogenerate tests, expand regression coverage, detect drift/anomalies, and reduce manual QA effort