Sidecar Health is redefining health insurance. Our mission is to make excellent healthcare affordable and accessible for everyone. We know that to accomplish this lofty mission, we need driven people who will make things happen.

The passionate people who make up Sidecar Health’s team come from all over, with backgrounds as tech leaders, policy makers, healthcare professionals, and beyond. And they all have one thing in common—the desire to fix a broken system and make it more personalized, affordable, and transparent.

If you want to use your talents to transform healthcare in the United States, come join us!

About the Role

Sidecar Health’s Business Analytics team is looking for a Staff Data Analytics Engineer to build the data foundation our product teams rely on. You’ll embed with Product and own the path from raw data to a metric people trust: designing data models, building pipelines when needed, defining the metrics that show whether product work is succeeding, and closing gaps in the data we collect. You’ll work in a Snowflake-first stack and use AI tools like Claude and Snowflake Cortex to move faster, with the judgment to validate what they produce. Our data environment is complex but modest in volume, with limited history, so you can shape the data model early. We operate in a regulated healthcare setting where privacy and compliance are built into how we work.

This is an individual contributor role reporting to the Senior Director, Business Analytics. It is a remote position but candidates must be based in California, Oregon, Washington, Utah, or Texas.

What You'll Do

• Design, build, and maintain dbt models and semantic models in Snowflake that give product teams a reliable, well-documented view of key metrics
• Build the pipelines behind those models when needed, from ingestion through transformation, and align with data engineering on shared infrastructure and standards
• Build AI-ready data: semantic models, metric definitions, and documentation that let Cortex and CoWork agents answer business questions accurately
• Guide data design decisions with Product and Engineering, including what data gets captured, how it’s structured, and how it’s modeled, so we can measure results from day one
• Partner with product managers to turn roadmap goals and feature hypotheses into measurable success metrics
• Identify gaps in data collection and drive them to resolution with Product and Engineering
• Provide analysis to inform product decisions and evaluate feature performance, including occasional statistical work such as experiment readouts and significance testing
• Use AI coding tools and Snowflake Cortex to speed up development, and review, test, and validate AI-generated code and outputs before they ship