Analytics Engineer (Senior)

Analytics EngineerAbout Sprinter HealthAt 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 RoleWe’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 LocationWe 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 doBuild canonical data models that create a shared source of truth across the companyDefine and maintain core business, operational, financial, product, and customer-facing metricsModel data in dbt or equivalent transformation tooling so dashboards, self-serve analytics, and customer reports pull from trusted tablesWrite tests, documentation, and data quality checks that catch issues before they reach usersCreate clear definitions for tables, fields, and metrics so teams understand what the data means and when to use itReconcile metric definitions across internal teams, external reporting needs, and payer customer expectationsTrace data lineage and debug dashboards, reports, or tables that change unexpectedlyPartner with analysts, data scientists, operations, finance, product, engineering, and customer-facing teams to understand data needs and translate them into reliable modelsHelp build reusable reporting frameworks that make onboarding new payers faster and less manualPartner with the data platform team to evolve warehouse tables, improve data architecture, and strengthen data contractsImprove warehouse cost, performance, and maintainabilitySupport PHI-aware data access patterns and help ensure sensitive healthcare data is modeled and used responsiblyWhat you have doneBuilt analytics engineering, business intelligence, or data modeling systems in a production cloud warehouse environmentWritten expert-level SQL and designed data models that support reporting, analysis, and decision-makingWorked with dbt or an equivalent transformation frameworkBuilt tested, documented, reusable data models rather than one-off queriesDefined, maintained, or reconciled business-critical metrics across teamsPartnered with analysts, data scientists, operators, finance teams, product teams, or customer-facing stakeholdersDebugged data quality issues, dashboard changes, metric discrepancies, and lineage problemsWorked with cloud data warehouses such as BigQuery, Snowflake, Redshift, Databricks SQL, or similarBalanced speed, correctness, usability, and maintainability when building data assetsCommunicated clearly with technical and non-technical stakeholders about what data means and how it should be usedWhat gives you an edgeYou have experience with healthcare data, claims data, EHR data, payer data, provider data, or other complex healthcare datasetsYou’ve worked with PHI, HIPAA-aware data access patterns, or other sensitive regulated dataYou have experience building customer-facing reporting, embedded analytics, or multi-tenant data modelsYou’ve worked with row-level security, access controls, or governed self-serve analyticsYou have experience using Python for analysis, scripting, data validation, or automationYou’ve helped establish a semantic layer, metrics layer, or company-wide source of truthYou’ve built data models in a high-growth startup or operationally complex environmentYou have experience improving warehouse performance, cost, and query efficiencyWhat makes you successfulYou treat a metric definition as a product artifact, not a Slack threadYou make data trustworthy, reusable, and easy to understandYou prevent metric chaos by building clear definitions, tests, and documentationYou build so that a fix in one place does not require five copy-paste edits elsewhereYou understand that internal users and external customers both need data they can trustYou care about the usability of the data model, not just whether the pipeline runsYou can explain data discrepancies clearly and drive teams toward shared definitionsYou build foundations that help the company move faster with more confidenceDay to DayIn this role, you might spend your time:Building or refactoring dbt modelsAdding tests to core tablesDefining canonical fields and documenting how they should be usedReviewing metric definitions and reconciling them across teamsDebugging a dashboard, report, or customer-facing metric that changed unexpectedlyTracing lineage from source systems through warehouse models to downstream reportsPartnering with analysts, operators, finance, product, or customer-facing teams on reporting needsImproving warehouse performance, cost, and maintainabilityDesigning reusable reporting structures that make new payer launches easierThe Interview ProcessWe aim to complete the interview process within 2–3 weeks. It will usually consist of:Recruiter Screen: Background fit, motivation, and compensation alignmentHiring Manager Interview: Analytics engineering experience, data modeling depth, and stakeholder partnershipHands-on Technical Assessment: SQL, data modeling, metric design, and practical analytics engineering judgmentOnsite Interview: Technical case study, systems/data modeling discussion, behavioral interview, and lunch with the teamReferences: Validation of performance, judgment, and working styleWhat we offerMeaningful pre-IPO equityMedical, dental, and vision plans 100% paid for you and your dependentsFlexible PTO + 10 paid holidays per year401(k) with match16-week parental leave policy for birthing parent, 8 weeks for all other parentsHSA + FSA contributionsLife insurance, plus short and long-term disability coverageFree daily lunch in-officeAnnual learning stipendRelocation assistanceEqual Opportunity StatementSprinter Health is an equal opportunity employer. We value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, disability status, or other protected classes.Beware of recruitment fraud and scams that involve fictitious job descriptions followed by false job offers.If you are applying for a job, you can confirm the legitimacy of a job posting by viewing current open roles on our official Sprinter Health Careers website.

All legitimate job postings will require an application to be made directly on our official Sprinter Health Careers website. Job-related communications will only be sent from email addresses ending in @sprinterhealth.com. Please ensure that you’re only replying to emails that end with @sprinterhealth.com.Compensation Range: $165K - $215KLocationSan Francisco, CA; Menlo Park, CAAddress394 Pacific Avenue , San Francisco, California, 94111Employment TypeFull timeLocation TypeHybridDepartmentSprinter HealthEngineeringCompensationSF Bay AreaEstimated Base Salary $165K – $215K • Offers Equity