Data Engineer III

Garner is on a mission to transform the U.S. healthcare system — and we're the only proven player doing exactly that. We partner with employers to redesign how healthcare works: applying 550+ proprietary clinical metrics across 80+ specialties to a dataset of 320M+ patients to identify the best-performing doctors, then using compelling incentives to steer members to the care that helps them get healthier, faster. The result is a rare "win win" — better care and lower costs for both members and employers. In just five years, our work has helped over 2.5 million people access higher-quality care and saved $1B in healthcare costs. We recently raised our Series E and have doubled five years running. If you've ever wanted your work to solve a problem that touches every person in this country, this is the opportunity to do exactly that. You'd be joining a team fundamentally reimagining healthcare in the U.S. — and using AI to scale that impact further and faster than anyone else can.

About the role

We are seeking an exceptional Data Engineer III to join our Engineering team, with a high sense of ownership who is excited about our mission, eager to learn and teach others, and can deliver across the tech stack. The ideal candidate should have experience with a modern data stack and implementations of data pipelines using Python and SQL in AWS.

Responsibilities

• Build, optimize, and maintain data pipelines that power our business
• Define and build out abstracted reusable data sets to be used for Business Intelligence, Marketing, and Data Science Research
• Design, build, and evangelize a federated data validation framework to be used to monitor potential data inconsistencies
• Protect our users' privacy and security through best practices

Requirements

• 2+ years of software/data engineering experience (distributed data processing, data warehousing, data governance, Big Data, data variance, data privacy, and data quality)
• Expertise in SQL and Python
• Expertise in building scalable data pipelines, query optimization, data modeling, and defining reusable datasets
• Experience