Job Description

We are seeking experienced GenRocket Synthetic Test Data SMEs/Engineers to establish and operationalize an enterprise-wide synthetic test data generation capability. The ideal candidate will have strong hands-on experience with GenRocket, Python, and Generative AI/LLMs, along with expertise in test data management, data modeling, and test automation integration.

Top 3 Responsibilities

1. Synthetic Test Data Generation: Establish enterprise-wide GenRocket capabilities and develop reusable scenarios and templates to generate realistic, referentially consistent test data.
2. Data Modeling and AI-Assisted Analysis: Analyze schemas, relationships, business rules, and data dependencies, leveraging Python and GenAI/LLMs to accelerate data discovery and model creation.
3. Automation Integration and Adoption: Integrate synthetic test data generation with test automation and CI/CD pipelines while supporting application teams with onboarding, implementation, and best practices.

Key Responsibilities

• Establish and operationalize enterprise synthetic test data generation capabilities using GenRocket.
• Analyze source data structures, schemas, relationships, business rules, constraints, and data dependencies.
• Develop reusable data models and GenRocket scenarios to generate realistic and referentially consistent synthetic test data.
• Create reusable templates, standards, and best practices for synthetic data generation.
• Use Python to automate test data generation, validation, and data processing activities.
• Leverage Generative AI and LLM-assisted techniques for data discovery, schema analysis, relationship identification, data model generation, and test scenario creation.
• Validate synthetic test data for accuracy, consistency, completeness, and compliance with business rules.
• Integrate synthetic test data generation processes with test automation frameworks and CI/CD pipelines.
• Collaborate with enterprise architecture, application development, QA, and data engineering teams to understand test data requirements.
• Support application teams with GenRocket onboarding, implementation, adoption, and troubleshooting.
• Establish reusable frameworks and documentation to improve test data generation efficiency and scalability.
• Ensure test data privacy and security requirements are addressed through appropriate synthetic data generation and masking practices.

Required Skills and Qualifications

• 8–10 years of relevant experience in test data management, test engineering, data engineering, or related technical roles.
• Strong hands-on experience with GenRocket and synthetic test data generation.
• Proficiency in Python for scripting, automation, and data validation.
• Experience with Generative AI and LLMs for data discovery, analysis, modeling, and test data scenario generation.
• Strong understanding of database schemas, data relationships, referential integrity, and business rules.
• Experience designing reusable data models, templates, and test data generation scenarios.
• Knowledge of test automation frameworks and CI/CD pipeline integration.
• Understanding of data privacy, data masking, and secure test data management practices.
• Excellent analytical, problem-solving, documentation, and communication skills.
• Ability to collaborate with enterprise, application, QA, and data teams.

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