QA Test/Automation Engineer

SimonsChustr; New York; Hybrid; USD 120,000 - 170,000; Full time

Job summary

Simon & Schuster, a world-leading publisher, is modernizing core systems for order management, fulfillment, partner integration, and financial transactions using .NET and SQL solutions with an AI-assisted approach to accelerate modernization while preserving data integrity across the platform.

The QA / Test Automation Engineer is the evidence engine of the program: building automated regression and data-reconciliation checks, running first-pass comparisons on parallel runs, and assembling the

Qualifications

• 5+ years of QA experience with a strong test-automation focus.
• Strong SQL skills with substantial data validation and reconciliation.
• Automated testing experience and integrating tests into pipelines.
• Data-centric testing across ETL/data pipelines or migrations.
• Experience using AI tools in testing or analysis.
• Root-cause discipline with clear communication of findings.
• Clear written communication for validation reports.

Responsibilities

• Own first-pass reconciliation: run parallel validation and classify discrepancies.
• Build automated regression and parity tests to preserve business behavior.
• Test integrations and performance with warehouse, EDI, and financial systems.
• Gate production cutovers with evidence and an auditable package.
• Coordinate UAT with business stakeholders and translate validation results.
• Harden the validation framework and data management for future replacements.
• Apply AI-assisted testing judiciously with rigorous validation.

Skills

Test strategy

SQL proficiency

Automated testing

Data-centric testing

AI tooling in testing

Root-cause analysis

Technical writing

Tools

NUnit/xUnit

pytest

AutoSys

Job description

About the Role

Simon & Schuster, one of the world's leading publishers, is investing in a multi-year modernization of the core systems that power order management, fulfillment, partner integration, and financial transactions. We are replacing long-established technology with modern .NET and SQL solutions using an AI-assisted approach.

The QA / Test Automation Engineer is the evidence engine of the program. Nothing replaces a production process here until it has been proven equivalent: every replacement runs in parallel with the existing system, writing to isolated validation tables, and its output is reconciled against the existing output row by row. This role owns that proof, building the automated regression and data-reconciliation checks, running the first-pass comparison on every parallel run, chasing every discrepancy to a root cause, and assembling the evidence package that gates each production cutover.

This is not a test-script-execution role. It is a data-heavy engineering role: most validation happens in SQL against large production datasets, wrapped in repeatable, automated harnesses, with AI tooling used to accelerate test generation and analysis.

Key Responsibilities

• Own first-pass reconciliation — Run and maintain the data-comparison process for every parallel ("shadow") run: legacy output vs. replacement output, row counts to column-level differences. Classify every discrepancy( defect, timing artifact, or explained difference) before it reaches business review.
• Build automated regression and parity tests — Create repeatable, automated test suites that verify converted functionality preserves required business behavior across releases, including edge cases identified during discovery.
• Test integrations and performance — Validate interfaces to warehouse, EDI, and financial systems; verify replacements meet or beat legacy runtimes and batch windows under production-scale volume.
• Gate production cutovers with evidence — Define and enforce the "green-run" standard (consecutive clean parallel runs); assemble the cutover evidence package reviewed by the business and the Modernization Lead.
• Coordinate business user testing — Organize UAT with business stakeholders alongside the Systems Analyst, translating validation results into terms business owners can sign off on.
• Harden the validation framework — Improve shared reconciliation procedures, test data management, and run-telemetry checks so each successive replacement is cheaper and safer to validate.
• Use AI tooling critically — Apply AI-assisted test generation, data analysis, and documentation, with the judgment to validate generated output before relying on it.

Required Qualifications

• 5+ years of QA experience with a strong test-automation focus, including ownership of test strategy for production systems (not solely manual test execution).
• Strong SQL skills — able to write substantial queries against large datasets for data validation and reconciliation: joins, aggregation, and set comparison across systems. This is the core daily skill of the role.
• Automated testing experience — building and maintaining automated test suites (e.g., NUnit/xUnit, pytest, or equivalent) and integrating them into a repeatable pipeline.
• Data-centric testing background — validating batch processes, ETL/data pipelines, or system migrations where correctness is proven by comparing datasets, not clicking through screens.
• Experience using AI tools in testing or analysis — e.g., AI-assisted test generation, data analysis, or documentation — with the judgment to critically validate generated output.
• Root-cause discipline — demonstrated ability to chase a data discrepancy through multiple systems to a definitive explanation, and to communicate findings clearly.
• Clear written communication — validation reports and evidence summaries that non-technical stakeholders can act on.

Preferred Qualifications

• Experience validating a system migration, replatforming, or legacy-modernization program (parallel-run / reconciliation-based testing).
• Exposure to legacy or mainframe-style environments (e.g., COBOL-era batch systems) — enough familiarity to work effectively with legacy specialists. No COBOL skills required.
• C# / .NET familiarity — able to read the code under test and write test harnesses in the team's stack.
• Order processing, fulfillment, supply-chain, or EDI domain experience.
• Performance and load testing of batch or high-volume data processes.
• Batch scheduling environments (AutoSys or similar) and overnight batch operations.
• Experience coordinating user acceptance testing with business stakeholders.

What Success Looks Like

• Every cutover is backed by a complete, auditable evidence package — and post-cutover defect rates stay near zero.
• Discrepancies are caught in parallel-run validation, not in production.
• Reconciliation and regression suites become faster and more reusable with each replacement package.
• Business stakeholders trust the validation process enough that sign-offs are routine, not contentious.