Analytics Engineer (Senior)

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 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

What we offer

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