Data Quality Assurance Architect

Role Overview: As a QA Architect for Data Products at Alegeus, you will be a senior individual contributor and technical lead responsible for ensuring the quality of data products from end to end. Your role involves shaping the testing strategy for application and AI products in collaboration with the QA Manager. You will play a crucial part in defining what AI-native testing looks like for a company that processes healthcare benefits at scale. Key Responsibilities: - Own the overall test strategy for data products, determining what needs to be tested, at which layer, with what data, and how results are validated against business expectations. - Define and evolve the testing strategy for application and AI products, covering test pyramid structure, tool selection, automation priorities, and release quality gates. - Design the test architecture for system and component layers, including services, contracts, APIs, and data flows, ensuring test execution integrates into CI/CD. - Architect the test data strategy, source data at each test layer, govern data refreshment, and ensure compliance with HIPAA and PII constraints. - Design and build synthetic data generation capabilities using AI tooling to create realistic, edge-case-rich datasets for testing. - Own testing across various customer configuration variations, ensuring test coverage spans the meaningful configuration space. - Define and implement data quality validation for all data products, including data pipelines, ETL/ELT workflows, reporting outputs, data APIs, and data platform services. - Oversee the testing strategy for AI/ML products, including evaluation harnesses, regression testing, model bias and drift detection, and output quality scoring. - Use AI tools daily for generating test cases, producing synthetic datasets, running exploratory test agents, and accelerating test design and defect analysis. - Write automation at system and component test levels, ensuring reliability and maintainability of test code. - Contribute to the shared automation platform and CI/CD integration maintained across teams. Qualifications Required: - 7-10 years of experience in software quality engineering with progression into technical leadership or architecture. - Background in product testing and validation of business behavior. - Hands-on experience in data product testing, synthetic data generation, and test data architecture. - Familiarity with highly configurable platforms and automation skills at system and component levels. - Active use of AI tools in testing practice and exposure to AI/ML product testing. - Bachelor's degree in Computer Science, Engineering, Information Systems, or equivalent practical experience. (Note: Additional details about the company were not included in the provided job description.) Role Overview: As a QA Architect for Data Products at Alegeus, you will be a senior individual contributor and technical lead responsible for ensuring the quality of data products from end to end. Your role involves shaping the testing strategy for application and AI products in collaboration with the QA Manager. You will play a crucial part in defining what AI-native testing looks like for a company that processes healthcare benefits at scale. Key Responsibilities: - Own the overall test strategy for data products, determining what needs to be tested, at which layer, with what data, and how results are validated against business expectations. - Define and evolve the testing strategy for application and AI products, covering test pyramid structure, tool selection, automation priorities, and release quality gates. - Design the test architecture for system and component layers, including services, contracts, APIs, and data flows, ensuring test execution integrates into CI/CD. - Architect the test data strategy, source data at each test layer, govern data refreshment, and ensure compliance with HIPAA and PII constraints. - Design and build synthetic data generation capabilities using AI tooling to create realistic, edge-case-rich datasets for testing. - Own testing across various customer configuration variations, ensuring test coverage spans the meaningful configuration space. - Define and implement data quality validation for all data products, including data pipelines, ETL/ELT workflows, reporting outputs, data APIs, and data platform services. - Oversee the testing strategy for AI/ML products, including evaluation harnesses, regression testing, model bias and drift detection, and output quality scoring. - Use AI tools daily for generating test cases, producing synthetic datasets, running exploratory test agents, and accelerating test design and defect analysis. - Write automation at system and component test levels, ensuring reliability and maintainability of test code. - Contribute to the shared automation platform and CI/CD integration maintained across teams. Qualifications Required: - 7-10 years of experience in software quality engineering wi