Technical Test Engineer – Synthetic Test Data

Veriipro; Milwaukee (WI); On-site; USD 140,000 - 190,000; Full time

Job summary

Veriipro in Milwaukee, WI is seeking an experienced GenRocket SME/Engineer to establish enterprise-wide synthetic test data capabilities. The role emphasizes hands-on GenRocket, Python scripting, and applying Generative AI/LLMs to accelerate data modeling, discovery, and test data generation.

You will design reusable data models and GenRocket scenarios, automate data generation and validation, and integrate with test automation frameworks and CI/CD pipelines, while ensuring data privacy and

Qualifications

• 8–10 years of experience in test data management, test engineering, data engineering, or related 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.

Responsibilities

• Establish 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.

Skills

GenRocket

Python

Generative AI/LLMs

Test Data Management

Data Modeling

Test Automation

Tools

GenRocket

Python

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.