Full 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 Technical Business Analyst connects business needs with hands-on data investigation. You will map processes, clarify requirements, and translate legacy behavior into specifications and acceptance criteria that developers can implement. Strong SQL skills are central to the role: you will compare existing and replacement outputs, explain differences, and help business users validate results before production cutover.
You will own comparison rules, test data, first-pass reconciliation, and validation evidence, working with developers who own the automation framework and application implementation. This is a technical analysis role with targeted business and data validation, not a dedicated test-automation engineering position.
Key Responsibilities
• Define requirements and processes - Work with business stakeholders to document current and future workflows, business rules, reporting needs, change impacts, and testable acceptance criteria.
• Trace data and system behavior - Investigate batch processes, interfaces, SQL logic, and reports with developers and legacy specialists; document data mappings, dependencies, and exceptions.
• Reconcile replacement outputs - Run established parallel-run comparisons, investigate record-level differences, and distinguish defects from timing artifacts or intended changes.
• Prepare test data and repeatable checks - Maintain representative datasets, expected results, and comparison queries; work with developers to extend validation coverage and retain regression checks for resolved defects.
• Validate reports and interfaces - Check calculations, totals, completeness, and exchanged data against agreed requirements; provide reproducible findings and follow issues through retesting.
• Coordinate UAT and cutover evidence - Organize business testing, maintain requirements-to-test traceability, and summarize results and unresolved issues so business owners can make informed acceptance decisions.
• Use AI tooling critically - Apply AI-assisted analysis, SQL generation, and documentation while checking generated output against actual system behavior and approved business rules.
Required Qualifications
• 5+ years of relevant technical business analysis, systems analysis, or data-focused QA experience, including requirements analysis and hands-on validation of production business systems.
• Strong SQL skills - independently write queries using joins, aggregation, and set comparisons; recognize data-grain, duplicate-record, missing-data, and timing issues that can distort reconciliation.
• Requirements and process analysis - translate stakeholder needs into process maps, functional and technical specifications, data definitions, and actionable acceptance criteria.
• Data-centric validation experience - investigate reports, batch processing, ETL/data pipelines, or migrations by comparing datasets and explaining discrepancies.
• Experience preparing test datasets, running established automated checks, coordinating UAT, and tracking issues through resolution.
• Clear communication with business users and engineers, supported by concise specifications, reproducible issue reports, and evidence-based conclusions.
• Experience using AI tools for analysis or testing, with the judgment to validate their output independently.
Preferred Qualifications
• Legacy-modernization or system-migration experience using parallel-run validation.
• Microsoft SQL Server, stored procedures, and reusable reconciliation queries.
• Order processing, fulfillment, supply-chain, financial transaction, or EDI experience.
• Exposure to COBOL-era batch environments or AutoSys; COBOL development skills are not required.
• C# / .NET or scripting familiarity for collaboration on existing validation checks.
What Success Looks Like
• Developers receive clear requirements, expected results, and reproducible issues.
• Differences are explained and resolved or approved before cutover.
• Business users trust reports, validation results, and the evidence supporting acceptance.
• Test data and reconciliation checks become easier to reuse with each replacement.
