Tech Stack

Description

You will own and elevate data quality across our data platforms and client solutions. You will lead complex data validation efforts, shape testing strategies, and act as a trusted partner to Engineering, Product, and Operations to ensure reliable, production-grade data solutions at scale.

Requirements

• Bachelor's degree in Computer Science, Health Science, or a related field (or equivalent practical experience).
• 5+ years of experience in data quality, data QA, or testing roles within data-driven environments; healthcare experience strongly preferred.
• Advanced SQL skills with the ability to validate, troubleshoot, and reason about complex datasets.
• Strong understanding of data pipelines, integrations, and large-scale data systems.
• Experience working with healthcare data domains such as enrollment, claims, and provider data.
• Demonstrated ability to lead QA efforts, influence cross-functional teams, and operate with a high degree of ownership.
• Excellent analytical, problem-solving, and prioritization skills in fast-moving environments.
• Strong communication skills, with the ability to explain data quality issues and risks to both technical and non-technical stakeholders.
• Deep understanding of QA methodologies, testing frameworks, and best practices.

Responsibilities

• Lead data quality strategy and execution across multiple projects and datasets.
• Partner closely with Engineering, Product, Operations, and Project Management to influence data design and quality outcomes early in the lifecycle.
• Perform advanced data analysis and validation on large-scale, client-specific and healthcare datasets.
• Design, execute, and review complex test plans and test cases, including automated and manual testing approaches.
• Identify systemic data quality risks, drive root-cause analysis, and partner with teams through resolution.
• Define, refine, and document QA standards, best practices, and verification protocols.
• Mentor junior QA engineers and provide guidance on testing methodologies and data validation techniques.
• Contribute to continuous improvement of QA processes, tooling, and workflows.