The ad says“You have experience with healthcare data, claims data, EHR data, payer data, provider data, or other complex healthcare datasets”
The ad says“Written expert-level SQL and designed data models that support reporting, analysis, and decision-making”
The ad says“Model data in dbt or equivalent transformation tooling so dashboards, self-serve analytics, and customer reports pull from trusted tables”
The ad says“You have experience with healthcare data, claims data, EHR data, payer data, provider data, or other complex healthcare datasets”
The ad says“You have experience with healthcare data, claims data, EHR data, payer data, provider data, or other complex healthcare datasets”
The ad says“You’ve worked with PHI, HIPAA-aware data access patterns, or other sensitive regulated data”
The ad says“You have experience with healthcare data, claims data, EHR data, payer data, provider data, or other complex healthcare datasets”
The ad says“You have experience with healthcare data, claims data, EHR data, payer data, provider data, or other complex healthcare datasets”
The ad says“You have experience using Python for analysis, scripting, data validation, or automation”
The ad says“Written expert-level SQL and designed data models that support reporting, analysis, and decision-making”
Values
The ad says“Worked with cloud data warehouses such as BigQuery, Snowflake, Redshift, Databricks SQL, or similar”
The ad says“Worked with cloud data warehouses such as BigQuery, Snowflake, Redshift, Databricks SQL, or similar”
The ad says“Worked with cloud data warehouses such as BigQuery, Snowflake, Redshift, Databricks SQL, or similar”
The ad says“Worked with cloud data warehouses such as BigQuery, Snowflake, Redshift, Databricks SQL, or similar”
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
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)
Preferred experience
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
Built tested, documented, reusable data models rather than one-off queries
Balanced speed, correctness, usability, and maintainability when building data assets
Defined, maintained, or reconciled business-critical metrics across teams
Written expert-level SQL and designed data models that support reporting, analysis, and decision-making
Worked with dbt or an equivalent transformation framework
Partnered with analysts, data scientists, operators, finance teams, product teams, or customer-facing stakeholders
Worked with cloud data warehouses such as BigQuery, Snowflake, Redshift, Databricks SQL, or similar
Built analytics engineering, business intelligence, or data modeling systems in a production cloud warehouse environment
Debugged data quality issues, dashboard changes, metric discrepancies, and lineage problems
Communicated clearly with technical and non-technical stakeholders about what data means and how it should be used
What you have done:
You have experience with healthcare data, claims data, EHR data, payer data, provider data, or other complex healthcare datasets
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 have experience improving warehouse performance, cost, and query efficiency
You’ve worked with row-level security, access controls, or governed self-serve analytics
You have experience building customer-facing reporting, embedded analytics, or multi-tenant data models
You’ve worked with PHI, HIPAA-aware data access patterns, or other sensitive regulated data
You’ve built data models in a high-growth startup or operationally complex environment
What gives you an edge:
Recruitment 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
