Sr. Data Quality Engineer

Verana Health, a digital health company that delivers quality drug lifecycle and medical practice insights from an exclusive real-world data network, recently secured a $150 million Series E led by Johnson & Johnson Innovation – JJDC, Inc. (JJDC) and Novo Growth, the growth-stage investment arm of Novo Holdings. 

Existing Verana Health investors GV (formerly Google Ventures), Casdin Capital, and Brook Byers also joined the round, as well as notable new investors, including the Merck Global Health Innovation Fund, THVC, and Breyer Capital.

We are driven to create quality real-world data in ophthalmology, neurology and urology to accelerate quality insights across the drug lifecycle and within medical practices. Additionally, we are driven to advance the quality of care and quality of life for patients. DRIVE defines our internal purpose and is the galvanizing force that helps ground us in a shared corporate culture. DRIVE is: Diversity, Responsibility, Integrity, Voice-of-Customer and End-Results. Click here to read more about our culture and values.

Our headquarters are located in San Francisco and we have additional offices in Knoxville, TN and New York City with employees working remotely in AZ, CA, CO, CT, FL, GA, IL, LA, MA, NC, NJ, NV, NY, OH, PA, SC, TN, TX, UT , VA, WA, Washington, D.C. All employees are required to have permanent residency in one of these states. Candidates who are willing to relocate are also encouraged to apply. 

Intro:

Verana Health is seeking a Sr. Healthcare Data Quality Engineer who understands how to solve complex business and health questions through data. This is a rare opportunity to help build the foundation of healthcare data and impact the lives of millions by helping reinvent healthcare research, clinical trials, and data curation.

The ideal candidate is someone who loves to learn, is detailed oriented, has exceptional critical thinking and analytical skills, has experience analyzing EHR or claims data, is proficient in SQL and Pyspark (Python/Pandas is acceptable), understands clinical workflows, and can quickly learn clinical concepts in ophthalmology, neurology, and urology. As a Sr. Data Quality Engineer, you will play a crucial role in ensuring the accuracy, reliability, and consistency of our data assets. 

Job Duties and Responsibilities:

• Designing and implementing data quality checks, identifying, and resolving data quality issues, collaborating with data engineers and product team to improve data pipelines, and developing data quality standards and best practices. 
• Work closely with cross-functional teams to implement and maintain data quality checks, identify, and resolve data quality issues, and contribute to the overall improvement of our data quality.
• Ensuring the accuracy, reliability, and consistency of our data assets. 
• Create data quality metrics, perform data profiling, monitor data quality trends, and provide recommendations for data quality improvements.
• Develop and maintain End to End data pipeline automation.
• Analyze architecture and designs to identify test automation strategies to provide full test coverage for new and/or existing features. Identify and articulate gaps in architecture or design.Overall, their goal is to validate, automate and maintain high-quality data that supports informed decision-making and business operations.

Basic Requirements:

• Bachelor’s degree in Data Analytics, Data Engineering, Information Technology or related field. 
• Experience working asynchronously in a remote, distributed team.
• Strong communication skills, with clear verbal communication as well as written communication skills.
• Design, develop, and implement data quality checks and validation processes to ensure the integrity of our data.
• Collaborate with data engineers, product team, and stakeholders to identify data quality issues and develop solutions.
• Perform data profiling and analysis to identify data anomalies, inconsistencies, and outliers.
• Monitor and track data quality trends and provide insights and recommendations for improvement.
• Establish and maintain data quality standards, best practices, and documentation.
• Strong understanding of data pipelines.
• Participate in the design and development of data pipelines to ensure data quality at every stage.
• Demonstrated experience of working with automated testing tools and automating tests across multiple platforms and technologies.
• Work with data engineers and quantitative scientists to develop automated tests.
• Create and maintain data quality metrics and reports for managemen