{"id":81681,"url":"https://alion.io/job/sprinter-health-analytics-engineer-senior","title":"Analytics Engineer (Senior)","company":{"id":5046,"name":"Sprinter Health","domain":"sprinterhealth.com","url":"https://alion.io/company/sprinter-health","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"B","score":82,"open_postings":15,"ghost_share":0,"stale_share":0.933,"repost_share":0,"time_to_fill_p50_days":18,"computed_at":"2026-10-17T05:46:00Z"}},"role":"Analytics","role_family":"Analytics","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Francisco, United States","Menlo Park, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":165000,"max":215000,"currency":"USD","period":"year","gross":null,"usd_annual":215000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon Redshift","optional":false},{"name":"BigQuery","optional":false},{"name":"Databricks","optional":false},{"name":"dbt","optional":false},{"name":"Google BigQuery","optional":false},{"name":"HIPAA","optional":false},{"name":"Python","optional":false},{"name":"Slack","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-07-20T22:07:44Z","employer_posted_date":"2026-07-20","last_verified_at":"2026-10-17T13:23:53Z","board_verified":true,"closed_at":null,"days_open":88,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":88},"description":"Analytics Engineer\nAbout Sprinter Health\nAt 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.\nBy 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.\nAbout the Role\nWe’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.\nAt 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.\nYou’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.\nThis 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.\nOffice Location\nWe 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.\nWe 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.\nLunch 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.\nWhat you will do\nBuild canonical data models that create a shared source of truth across the company\n\nDefine and maintain core business, operational, financial, product, and customer-facing metrics\n\nModel data in dbt or equivalent transformation tooling so dashboards, self-serve analytics, and customer reports pull from trusted tables\n\nWrite tests, documentation, and data quality checks that catch issues before they reach users\n\nCreate clear definitions for tables, fields, and metrics so teams understand what the data means and when to use it\n\nReconcile metric definitions across internal teams, external reporting needs, and payer customer expectations\n\nTrace data lineage and debug dashboards, reports, or tables that change unexpectedly\n\nPartner with analysts, data scientists, operations, finance, product, engineering, and customer-facing teams to understand data needs and translate them into reliable models\n\nHelp build reusable reporting frameworks that make onboarding new payers faster and less manual\n\nPartner with the data platform team to evolve warehouse tables, improve data architecture, and strengthen data contracts\n\nImprove warehouse cost, performance, and maintainability\n\nSupport PHI-aware data access patterns and help ensure sensitive healthcare data is modeled and used responsibly\n\nWhat you have done\nBuilt analytics engineering, business intelligence, or data modeling systems in a production cloud warehouse environment\n\nWritten expert-level SQL and designed data models that support reporting, analysis, and decision-making\n\nWorked with dbt or an equivalent transformation framework\n\nBuilt tested, documented, reusable data models rather than one-off queries\n\nDefined, maintained, or reconciled business-critical metrics across teams\n\nPartnered with analysts, data scientists, operators, finance teams, product teams, or customer-facing stakeholders\n\nDebugged data quality issues, dashboard changes, metric discrepancies, and lineage problems\n\nWorked with cloud data warehouses such as BigQuery, Snowflake, Redshift, Databricks SQL, or similar\n\nBalanced speed, correctness, usability, and maintainability when building data assets\n\nCommunicated clearly with technical and non-technical stakeholders about what data means and how it should be used\n\nWhat gives you an edge\nYou have experience with healthcare data, claims data, EHR data, payer data, provider data, or other complex healthcare datasets\n\nYou’ve worked with PHI, HIPAA-aware data access patterns, or other sensitive regulated data\n\nYou have experience building customer-facing reporting, embedded analytics, or multi-tenant data models\n\nYou’ve worked with row-level security, access controls, or governed self-serve analytics\n\nYou have experience using Python for analysis, scripting, data validation, or automation\n\nYou’ve helped establish a semantic layer, metrics layer, or company-wide source of truth\n\nYou’ve built data models in a high-growth startup or operationally complex environment\n\nYou have experience improving warehouse performance, cost, and query efficiency\n\nWhat makes you successful\nYou treat a metric definition as a product artifact, not a Slack thread\n\nYou make data trustworthy, reusable, and easy to understand\n\nYou prevent metric chaos by building clear definitions, tests, and documentation\n\nYou build so that a fix in one place does not require five copy-paste edits elsewhere\n\nYou understand that internal users and external customers both need data they can trust\n\nYou care about the usability of the data model, not just whether the pipeline runs\n\nYou can explain data discrepancies clearly and drive teams toward shared definitions\n\nYou build foundations that help the company move faster with more confidence\n\nDay to Day\nIn this role, you might spend your time:\nBuilding or refactoring dbt models\n\nAdding tests to core tables\n\nDefining canonical fields and documenting how they should be used\n\nReviewing metric definitions and reconciling them across teams\n\nDebugging a dashboard, report, or customer-facing metric that changed unexpectedly\n\nTracing lineage from source systems through warehouse models to downstream reports\n\nPartnering with analysts, operators, finance, product, or customer-facing teams on reporting needs\n\nImproving warehouse performance, cost, and maintainability\n\nDesigning reusable reporting structures that make new payer launches easier\n\nThe Interview Process\nWe aim to complete the interview process within 2-3 weeks. It will usually consist of:\nRecruiter Screen: Background fit, motivation, and compensation alignment\n\nHiring Manager Interview: Analytics engineering experience, data modeling depth, and stakeholder partnership\n\nHands-on Technical Assessment: SQL, data modeling, metric design, and practical analytics engineering judgment\n\nOnsite Interview: Technical case study, systems/data modeling discussion, behavioral interview, and lunch with the team\n\nReferences: Validation of performance, judgment, and working style\n\nWhat we offer\nMeaningful pre-IPO equity\n\nMedical, dental, and vision plans 100% paid for you and your dependents\n\nFlexible PTO + 10 paid holidays per year\n\n401(k) with match\n\n16-week parental leave policy for birthing parent, 8 weeks for all other parents\n\nHSA + FSA contributions\n\nLife insurance, plus short and long-term disability coverage\n\nFree daily lunch in-office\n\nAnnual learning stipend\n\nRelocation assistance\n\nEqual Opportunity Statement\nSprinter 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.\nBeware of recruitment fraud and scams that involve fictitious job descriptions followed by false job offers.\nIf 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. 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