Software Engineer

Implement, configure, and maintain enterprise data masking solutions using tools such as IBM Optim.

Perform data discovery and profiling across structured and unstructured data sources to identify and classify sensitive information.

Design masking rules that preserve data realism and referential integrity across complex relational data models.

Ensure PII and sensitive financial data are appropriately protected in all non-production environments.

Design and build synthetic data sets that accurately mimic production data characteristics, edge cases, and complex business scenarios without exposing real customer information.

Use TDM tools such as Tonic Fabricate and custom Python or JavaScript scripts to generate realistic, referentially intact data.

Collaborate with business analysts and QA teams to understand data requirements and translate them into technically accurate, repeatable data generation scripts and workflows.

Create synthetic data for functional, integration, API, and end-to-end testing.

Develop and maintain automated tests and data-setup workflows using Playwright and JavaScript/TypeScript.

Integrate test data creation, validation, and cleanup into automated testing frameworks.

Build reusable utilities and fixtures to establish complex test data states.

Manage and maintain test data across a wide variety of database platforms, including relational and NoSQL systems.

Write complex SQL queries, stored procedures, and scripts to extract, transform, subset, and load data across multiple environments and schemas.

Execute environment data refreshes, ensuring test databases are populated with the correct, masked, and complete data sets aligned to each testing phase.

Maintain referential integrity across complex, multi-system data models spanning legacy platforms (LA) and modern platforms (Alfa, FiServ).

Build automated test data pipelines that provision data on-demand as part of CI/CD workflows (Jenkins, GitLab, GitHub Actions), eliminating manual data setup bottlenecks.

Write Python scripts to automate data generation, transformation, validation, and delivery into target environments at scale.

Build self-service data provisioning capabilities that allow QA engineers to request and receive test data instantly, without manual TDM team intervention.

Implement automated data validation checks to ensure that provisioned data is complete, accurate, and fit-for-purpose before test cycles begin.

Use REST and SOAP APIs to create, retrieve, update, and delete test data programmatically.

Automate API-chaining workflows to establish multi-system data states for end-to-end testing.

Build and maintain mock APIs and service virtualization stubs for unavailable third-party or downstream services.

Validate JSON and XML payloads against application contracts and business rules.

Support API automation using Postman, RestAssured, Python, JavaScript, and Playwright.

Maintain up-to-date documentation on data models, masking rules, data dictionaries, pipeline configurations, and known data constraints.

Continuously identify and drive improvements to TDM processes, tooling, and automation to enhance data delivery speed, quality, and security.

Develop and maintain runbooks for all repeatable TDM processes to enable team scalability and knowledge sharing.

4–5 years of hands-on experience in Test Data Management, Data Engineering, Test Automation, or a closely related field, preferably in financial services or fintech. 

Strong experience with modern testing technologies, including: 

JavaScript 

Playwright

API and end-to-end test automation

Modern automation frameworks and practices

Experience with enterprise TDM tools such as IBM Optim, Informatica TDM, K2view, Delphix, or Tonic. 

Strong SQL proficiency, including complex joins, subqueries, stored procedures, data validation, and performance tuning. 

Experience working with databases such as Oracle, SQL Server, PostgreSQL, and DB2. 

Hands-on Python experience for data automation, synthetic data generation, transformation, and pipeline orchestration. 

Strong experience with REST and SOAP APIs, JSON/XML payloads, API chaining, and tools such as Postman, RestAssured, Python Requests, or Playwright. 

Good understanding of data masking, obfuscation, synthetic data, and PII protection in non-production environments. 

Experience integrating data and automation pipelines with Jenkins, GitLab CI, or GitHub Actions. 

Understanding of relational data modeling, referential integrity, and complex multi-table relationships. 

Strong analytical and problem-solving skills, with the ability to independently troubleshoot complex data issues. 

Effective communication and collaboration skills across QA, development, infrastructure, product, and business teams.

Experience with service virtualization and mocking downstream dependencies.

Familiarity with NoSQL databases and unstructured data.

Knowledge of current TDM tools, frameworks, and market trends, particularly within fintech.

Exposure to cloud-based data platforms and containerized applications.

Mainframe experience, including DB2, JCL, COBOL, or related technologies, is beneficial but not mandatory.