Role: Senior QA Automation Engineer with CDO CI/CD, Data & Agentic AI
Location: Middletown, NJ (Onsite)
Duration: 12 months with renewals
Action Item: I recommend searching for candidates with SDET titles who specifically list Pytest, Snowflake, and GitHub Actions to meet the technical bar set by the CDO team.
Experience Level: Senior QA Automation Engineer
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 AT&T 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 AT&T 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
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