Garner Health
Data Engineer III
About this role
Garner seeks a Data Engineer II to build and optimize data pipelines that support healthcare analytics and business intelligence. You'll design scalable data infrastructure, create reusable datasets, and implement data validation frameworks while ensuring privacy and security compliance in a mission-driven healthcare technology company.
What you'll do
- Build, optimize, and maintain data pipelines powering business operations
- Create abstracted, reusable datasets for Business Intelligence, Marketing, and Data Science teams
- Design and implement a federated data validation framework to monitor data inconsistencies
- Ensure user privacy and security through best practices and compliance standards
- Work with modern data stack tools including Snowflake, Airflow, and Python
- Collaborate with cross-functional teams on data architecture and quality
What they're looking for
- SQL and Python
- Data pipeline design and optimization
- Snowflake or other data warehouse platforms
- Apache Airflow or similar orchestration tools
- PostgreSQL and relational databases
- AWS and cloud infrastructure
- Data modeling and query optimization
- HIPAA compliance and data security
Benefits
- Competitive salary range: $166,000 - $205,000
- Equity incentive participation
- Flexible PTO
- Medical, Dental, and Vision plan options
- 401(k) with company match
- Teladoc Health and additional wellness benefits
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Garner Health
Garner Health builds healthcare analytics systems that process medical data at scale using AI and distributed architectures. The company is hiring Software Engineers and Data Engineers to develop mission-critical infrastructure, data pipelines, and business intelligence solutions while maintaining privacy and security compliance.
- Website
- garnerhealth.com
Likely interview questions
- Walk us through your experience building and optimizing data pipelines in production. What tools did you use, and how did you approach performance optimization?
- Describe your experience with Airflow or other orchestration tools. How have you handled scheduling, error handling, and monitoring in complex workflows?