Junior Data Test Analyst (26-07257) -

Position Full time

Junior Data Test Analyst

Location

Hamilton, NJ (Onsite)

Duration

FULL TIME

Job Description

• Up to 5 years of experience in Data Testing or SDET roles with strong business and data focus
• Strong understanding of wealth management or financial services domains
• Advanced SQL for data validation and reconciliation

Experience validating

Backend systems, APIs, and integrations from a data perspective

Batch processing and data pipelines

Support business-critical data quality across wealth management products by validating financial data, backend services, APIs, integrations, batch processing, and data pipelines. The role combines strong domain understanding with advanced data-testing and reconciliation skills.

Must-Have Technical and Functional Skills

• Demonstrated experience in Data Testing or SDET roles with a strong business and data focus.
• Strong understanding of wealth management or financial services domains.
• Advanced SQL skills for data validation and reconciliation.
• Experience validating backend systems, APIs, and integrations from a data perspective.
• Experience validating batch processing and data pipelines.
• Ability to understand and test business rules, calculations, and financial logic, not only data schemas.
• Hands-on experience with Python and PyTest for data-testing automation is an added advantage.

Roles and Responsibilities

• Business- and Domain-Driven Data Testing
• Develop a strong functional understanding of wealth management products and data, including accounts, positions, holdings, transactions, balances, statements, fees, performance, valuations, and corporate actions.
• Understand business workflows and data flows end to end.
• Translate business rules and requirements into testable data-validation scenarios.
• Data Testing and Reconciliation
• Design and execute data-validation and reconciliation tests for financial transactions and balances.
• Validate data across backend systems, APIs, integrations, batch processes, and data pipelines.
• Use SQL to identify discrepancies, reconcile results, and verify expected business outcomes.
• Collaboration and Defect Analysis
• Partner with business, product, and engineering teams to clarify requirements and expected data behavior.
• Investigate data issues with a business-first mindset.
• Provide clear explanations of data breaks and mismatches.
• Document defects with business context, supporting data evidence, and root-cause insights