What are the responsibilities and job description for the Senior Data QA Engineer - REMOTE position at Sierra Solutions?
Job Summary
We are looking for a Senior Data QA Engineer for our Healthcare client to join the Data Science team. The ideal candidate will be crucial in ensuring the accuracy, consistency, and reliability of data products and analytical tools, specifically focusing on healthcare data. As a Senior Data QA Engineer, you will leverage 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. Candidates must have healthcare data experience.
Primary Responsibilities
Education and Experience
We are looking for a Senior Data QA Engineer for our Healthcare client to join the Data Science team. The ideal candidate will be crucial in ensuring the accuracy, consistency, and reliability of data products and analytical tools, specifically focusing on healthcare data. As a Senior Data QA Engineer, you will leverage 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. Candidates must have healthcare data experience.
Primary Responsibilities
- 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 products.
- Define test requirements by leveraging historical data, statistical analysis, and machine learning techniques to ensure accuracy and reliability.
- 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.
Education and Experience
- 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.
