Senior Analytics Engineer, Sprinter Health
$165-215k
Offers equity
More information about location
4+ days a week in office
On demand, at-home health tests
Be an early applicant
On demand, at-home health tests
201-500 employees
Be an early applicant
$165-215k
Offers equity
More information about location
4+ days a week in office
201-500 employees
Company mission
To dramatically expand and untether access to healthcare by reimagining the patient experience—delivered at home and powered by technology for scale.
Role
Who you are
- 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
- 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 the job involves
- 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
- 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 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
- 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
Application process
- We aim to complete the interview process within 2–3 weeks. It will usually consist of:
- Recruiter Screen: Background fit, motivation, and compensation alignment
- Hiring Manager Interview: Analytics engineering experience, data modeling depth, and stakeholder partnership
- Hands-on Technical Assessment: SQL, data modeling, metric design, and practical analytics engineering judgment
- Onsite Interview: Technical case study, systems/data modeling discussion, behavioral interview, and lunch with the team
- References: Validation of performance, judgment, and working style
Our take
Sprinter Health offers on-demand health checks and lab draws by sending nurses and phlebotomists (employed by Sprinter itself) into the home. Amongst its services are blood draws and urine samples, mobile imaging and vitals checks, as well as assistance with telehealth consultations.
The rise in remote, digital health solutions is helping to bring greater access to care, but it’s also created a need for last-mile, in-person services. Indeed, while the last year or so has actually seen a marked drop in investment in digital health, home-based care providers are still going strong.
There’s no doubt that the COVID-19 pandemic had a lot to do with this, but it appears that the demand for in-home services is here to stay. If anything, they’re something of a throwback to decades gone by when house calls were a regular occurrence.
Despite only emerging from stealth in late 2021, Sprinter is very well funded and has been quickly building up its headcount in California, with a long-term focus on national expansion.
Steph
Company Specialist at Welcome to the Jungle
Insights
36% employee growth in 12 months
Company
Funding (last 2 of 3 rounds)
May 2025
$55m
SERIES B
Oct 2021
$33m
SERIES A
Total funding: $92.6m
Company benefits
- Free lunch everyday and fully-stocked microkitchens and coffee/tea bar
- Team offsites, game hours, tech talks, and design sprints
- Health insurance benefits (PPO / HMO / HSA, 100% of family premiums paid)
- Dental / vision / wellness benefits
- 401k matching
- Unlimited PTO
- Flexible work-from-home policy for work/life balance (4 days in-office, accommodation for occasional WFH days)
- Relocation assistance
- Generous parental leave (4 months for the birthing parent, 3 months for a partner, 100% paid)
Company HQ
The Castro, San Francisco, CA
Leadership
Max Cohen
(Co-Founder & CEO)Worked at Google from 2011 to 2014 as a Product Manager, before joining Oculus VR as VP of Mobile, a few months prior to Facebook's acquisition of the company. Continued at Oculus VR until 2019 as Head of Mobile, Head of Oculus Product for Mobile and Platform, and Head of AR/VR Media Product.
Cameron Behar
(Co-Founder & CTO)Like Sprinter Co-founder Max Cohen, previously worked for Google and Facebook. Worked as a Software Engineer and Tech Leads across a number of verticals at Google. Worked at Oculus after Facebook's acquisition as a Tech Lead Manager and Engineering Manager.
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