This role demands a technically deep, detail-oriented engineer who can automate data pipelines, work across complex legacy and modern database systems, and collaborate with diverse engineering and business teams in a fast-paced fintech environment. **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. + Valuable 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.**

📌 Software Engineer (Chennai)
🏢 Ford Motor
📍 Chennai