Job ID

67929

Category

Ford Credit Services

Location

Chennai, India

Work Type

Hybrid

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.

- Robust 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.

📌 Software Engineer (India)
🏢 Ford Motor
📍 India