Data Engineer II
Garner Health is seeking a Data Engineer II to join our Engineering team in New York City. This role involves building, optimizing, and maintaining data pipelines that power our business, as well as defining and building reusable datasets for Business Intelligence, Marketing, and Data Science Research. The position requires working in the office three days per week on Tuesday, Wednesday, and Thursday.
Key responsibilities include designing and implementing scalable data pipelines, developing a federated data validation framework to monitor potential data inconsistencies, and ensuring user privacy and security through best practices. The role also involves collaborating with cross-functional teams to support data-driven decision-making processes.
The ideal candidate has over two years of experience in software or data engineering, with expertise in SQL and Python. Experience with orchestration tools like Airflow, databases such as PostgreSQL, and data warehouses like Snowflake is essential. Familiarity with healthcare or insurance industries, data security, and HIPAA compliance is preferred. A strong sense of ownership, eagerness to learn and teach others, and the ability to deliver across the tech stack are crucial qualities for this role.
The target salary range for this position is $125,000 to $165,000, depending on qualifications and experience. In addition to base compensation, this role is eligible to participate in our equity incentive and competitive benefits plans, including flexible PTO, medical/dental/vision plan options, 401(k) with company match, flexible spending accounts, and Teladoc Health services.
Garner Health is a mission-driven company focused on transforming the healthcare economy by delivering high-quality and affordable care for all. We value a high-performing team culture where everyone is expected to deliver exceptional results and embrace the hard work needed to accomplish our audacious goals. Joining our team offers the opportunity to make a meaningful impact on healthcare at scale.