Analytics Engineer (Senior)

Sprinter Health•San Francisco, CA

2d•Hybrid

About The Position

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.

Requirements

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

Nice To Haves

Experience with healthcare data, claims data, EHR data, payer data, provider data, or other complex healthcare datasets
Worked with PHI, HIPAA-aware data access patterns, or other sensitive regulated data
Experience building customer-facing reporting, embedded analytics, or multi-tenant data models
Worked with row-level security, access controls, or governed self-serve analytics
Experience using Python for analysis, scripting, data validation, or automation
Helped establish a semantic layer, metrics layer, or company-wide source of truth
Built data models in a high-growth startup or operationally complex environment
Experience improving warehouse performance, cost, and query efficiency

Responsibilities

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

Benefits

Meaningful pre-IPO equity
Medical, dental, and vision plans 100% paid for you and your dependents
Flexible PTO + 10 paid holidays per year
401(k) with match
16-week parental leave policy for birthing parent, 8 weeks for all other parents
HSA + FSA contributions
Life insurance, plus short and long-term disability coverage
Free daily lunch in-office
Annual learning stipend
Relocation assistance