About the role
Analytics Engineer About Sprinter Health At Sprinter Health, our mission is reimagining how people access care by bringing it directly to their homes. Nearly 30% of patients in the U.S. skip preventive or chronic care simply because they can’t get to a doctor’s office. For many, the ER becomes their first touchpoint with the healthcare system, driving over $300B in avoidable costs every year. By using the same technologies that power leading marketplace and last-mile platforms, we deliver care where people are, especially those who need it most. So far, we’ve supported more than 2 million patients across 22 states, completed 130,000+ in-home visits, and maintained a 92 NPS. Our team of clinicians, technologists, and operators has raised over $125M from investors like a16z, General Catalyst, GV, and Accel and enjoys multi-year runway.
About
the Role We’re looking for an Analytics Engineer to build the trusted data layer that analysts, data scientists, operations, finance, product, and our payer customers depend on. At Sprinter, data is central to how we operate, measure performance, serve patients, and support our health plan partners. This role will own the canonical models, metric definitions, transformation logic, documentation, and tests that make our data reliable and reusable across the company. You’ll help define what each table, field, and metric means, then build the infrastructure that ensures those definitions are consistently applied. That includes modeling data in dbt or equivalent tooling, creating reporting-ready tables, improving lineage and documentation, reconciling metrics across teams, and helping prevent the kind of data drift and metric chaos that slows companies down as they scale. This role is ideal for someone who treats metric definitions as product artifacts, thinks in contracts and tests, and cares deeply about making data trustworthy for both internal users and external customers. Office Location We are a hybrid company based in the Bay Area with offices in both San Francisco and Menlo Park. We operate on a hybrid schedule, working from the office Monday through Thursday, with Fridays designated as work-from-anywhere days. We care deeply about work-life balance and are happy to provide flexibility when life happens. We ask that employees be in the office Monday through Thursday to collaborate with their teams while maintaining flexibility where it matters most. Lunch is provided every day, and the entire team takes an hour to eat together. It’s one of the ways we stay connected outside of meetings. You’ll usually find us playing a board game before getting back to work. What you will do Build canonical data models that create a shared source of truth across the company Define and maintain core business, operational, financial, product, and customer-facing metrics Model data in dbt or equivalent transformation tooling so dashboards, self-serve analytics, and customer reports pull from trusted tables Write tests, documentation, and data quality checks that catch issues before they reach users Create clear definitions for tables, fields, and metrics so teams understand what the data means and when to use it Reconcile metric definitions across internal teams, external reporting needs, and payer customer expectations Trace data lineage and debug dashboards, reports, or tables that change unexpectedly Partner with analysts, data scientists, operations, finance, product, engineering, and customer-facing teams to understand data needs and translate them into reliable models Help build reusable reporting frameworks that make onboarding new payers faster and less manual Partner with the data platform team to evolve warehouse tables, improve data architecture, and strengthen data contracts Improve warehouse cost, performance, and maintainability Support PHI-aware data access patterns and help ensure sensitive healthcare data is modeled and used responsibly What you have done Built analytics engineering, business intelligence, or data modeling systems in a production cloud warehouse environment Written expert-level SQL and designed data models that support reporting, analysis, and decision-making Worked with dbt or an equivalent transformation framework Built tested, documented, reusable data models rather than one-off queries Defined, maintained, or reconciled business-critical metrics across teams Partnered with analysts, data scientists, operators, finance teams, product teams, or customer-facing stakeholders Debugged data quality issues, dashboard changes, metric discrepancies, and lineage problems Worked with cloud data warehouses such as BigQuery, Snowflake, Redshift, Databricks SQL, or similar Balanced speed, correctness, usability, and maintainability when building data assets Communicated clearly with technical and non-technical stakeholders about what data means and how it should be used What gives you an edge You have experience with healthcare data, claims data, EHR data, payer data, provider data, or other complex healthcare datasets You’ve worked with PHI, HIPAA-aware data access patterns, or other sensitive regulated data You have experience building customer-facing reporting, embedded analytics, or multi-tenant data models You’ve worked with row-level security, access controls, or governed self-serve analytics You have experience using Python for analysis, scripting, data validation, or automation You’ve helped establish a semantic layer, metrics layer, or company-wide source of truth You’ve built data models in a high-growth startup or operationally complex environment You have experience improving warehouse performance, cost, and query efficiency What makes you successful You treat a metric definition as a product artifact, not a Slack thread You make data trustworthy, reusable, and easy to understand You prevent metric chaos by building clear definitions, tests, and documentation You build so that a fix in one place does not require five copy-paste edits elsewhere You understand that internal users and external customers both need data they can trust You care about the usability of the data model, not just whether the pipeline runs You can explain data discrepancies clearly and drive teams toward shared definitions You build foundations that help the company move faster with more confidence Day to Day In this role, you might spend your time: Building or refactoring dbt models Adding tests to core tables Defining canonical fields and documenting how they should be used Reviewing metric definitions and reconciling them across teams Debugging a dashboard, report, or customer-facing metric that changed unexpectedly Tracing lineage from source systems through warehouse models to downstream reports Partnering with analysts, operators, finance, product, or customer-facing teams on reporting needs Improving warehouse performance, cost, and maintainability Designing reusable reporting structures that make new payer launches easier The Interview Process We aim to complete the interview process within 2–3 weeks. It will usually consist of: Recruiter Screen: Background fit, motivation, and