Data Masking & Obfuscation:

• 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.

Synthetic Data Generation:

• 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.

Modern Test Automation

• 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.

Database Management & Data Provisioning:

• 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).

Automation & CI/CD Integration:

• 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.

API-Based Data Management:

• 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.

Documentation & Process Improvement:

• 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.

Preferred Skill:

• 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.