Senior Data QA Engineer

Position Overview:

Trella Health is seeking a highly skilled and experienced Senior Data QA Engineer to join our Data Science team. The ideal candidate will play a crucial role in ensuring the accuracy, consistency, and reliability of our data products and analytical tools, with a specific focus on healthcare data. As a Senior Data QA Engineer, you will leverage both machine learning and other techniques to automate and enhance the data QA process, working closely with data scientists, engineers, and other stakeholders to ensure that our data meets the highest standards.

Location: Remote - US (Preference to applicants located in GA, TN, SC, NC, FL, TX & PA)

Reports to: Manager, Data Science

As a Senior Data QA Engineer at Trella, you will:

• Develop, implement, maintain, and document a comprehensive data QA process and framework. Continuously refine and optimize the data QA process and quickly integrate new data sources.
• Create new and maintain existing documentation for testing Trella Health’s products.
• Test data and solutions against business requirements to ensure alignment of Trella Health’s business needs, the written requirements, and the delivered solution.
• Validate data integrity, consistency, and accuracy across various data sources, databases, and data products.
• Conduct data validation and verification processes to ensure compliance with business rules, data definitions, and industry standards.
• Perform data profiling and root cause analysis to identify data anomalies, inconsistencies, and quality issues.
• Leverage machine learning techniques to automate and enhance the data QA process.
• Utilize knowledge of healthcare claims data and outcome measures to ensure high data quality standards.
• Proactively assess risks and help to find issues as early as possible.
• Stay informed of industry best practices and emerging trends in data QA tools and methodologies.
• Guide team members on QA best practices and findings by sharing expertise, providing feedback, and regularly meeting with data scientists and analysts about QA output.
• Provide clear and concise feedback to data scientists and other stakeholders on data quality issues, test results, and overall data quality metrics.
• Work closely with cross-functional teams to understand data requirements, use cases, and technical specifications.
• Actively participate in Agile/Scrum processes, including sprint planning, daily stand-ups, retrospectives, and all stages of the release process—contributing to release planning, coordinating testing and deployment, and ensuring smooth delivery of each release to production.
• Identify opportunities for process improvements and contribute to the development of data quality standards and practices.

This job might be a fit for you if you have: 

• Experience with healthcare claims data and quality metrics.
• Minimum of 5 years of experience in data quality assurance or data analysis.
• Proven experience with data validation, data profiling, and root cause analysis.
• In-depth knowledge of data warehousing, ETL processes, and data pipeline architectures.
• Ability to manage, analyze, and derive insights from large, complex custom datasets.
• Experience with programming languages such as Python and SQL for data validation and automation.
• Experience with machine learning techniques and tools to automate and enhance QA processes.
• Strong analytical and problem-solving skills with a focus on data-driven decision-making.
• Excellent verbal and written communication skills with the ability to convey and document complex technical concepts.
• Ability to work both independently and in a collaborative environment.
• Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field.