Senior Data QA Engineer

At Trella Health, we are passionate and committed to our mission – empowering meaningful change in healthcare. Since our founding in 2015, we continue to grow our team, enhance our solution and services offerings, accelerate into new markets, and expand our customer base. We are rapidly growing and are looking for new Trellavators to join our team!

“What is a Trellavator?” you ask. Innovate and elevate is the name of our game! We go above and beyond to collaborate with and support each other – we believe that when a colleague or customers succeeds, we succeed. By learning from others, building on our successes, and taking risks, we constantly raise the bar – continuous improvement is in our DNA. Our word is paramount, we keep our commitments, and we always follow through. We have a strong, reliable support system that fuels growth, collaboration, and passion – and together, we create a positive environment where everyone at Trella Health, including the customers we support, can thrive. Are you ready to learn more about the opportunities with our team? Trell-yeah you are!

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 d