Software Engineer-TDM

Data Masking & Obfuscation:

Implement, configure, and maintain enterprise data masking solutions using tools such as IBM Optim to ensure PII and sensitive financial data is fully obfuscated in all non-production environments.

Perform data discovery and profiling across structured databases (SQL Server, Oracle, DB2) and unstructured sources to classify sensitive data elements.

Design and enforce masking rules that maintain referential integrity and data realism across complex, multi-table relational data models.

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.

Leverage TDM tools (e.g., Tonic Fabricate) and custom Python scripting to generate large volumes of realistic, referentially intact synthetic data on demand.

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

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 records in target applications and databases programmatically.

Automate API chaining workflows to set up complex, multi-system data states required for End-to-End (E2E) testing scenarios.

Build and maintain Mock API responses and service virtualization stubs to simulate third-party data dependencies when live downstream systems are unavailable.

Collaborate with development teams to understand API data contracts and ensure synthetic/masked data conforms to required payload structures and validation rules.

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.

3+ years of hands-on experience in Test Data Management, Data Engineering, or a closely related field, preferably within financial services or fintech.

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

Strong proficiency in SQL, including complex joins, subqueries, stored procedures, and performance tuning across multiple database platforms (Oracle, SQL Server, DB2, PostgreSQL).

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

Strong experience with API testing and API-based data management using tools such as Postman, RestAssured, or Python (Requests library), including REST/SOAP API interaction, JSON/XML payload handling, and API chaining.

Deep understanding of data masking, data obfuscation, and PII handling in non-production environments.

Experience integrating test data pipelines into CI/CD workflows (Jenkins, GitLab CI, GitHub Actions).

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

Good communication and collaboration skills, with the ability to work effectively with QA, development, infrastructure, and business stakeholders.

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