Analytics Engineer (Senior)
About The Position
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.
Requirements
- 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
Nice To Haves
- Experience with healthcare data, claims data, EHR data, payer data, provider data, or other complex healthcare datasets
- Experience with PHI, HIPAA-aware data access patterns, or other sensitive regulated data
- Experience building customer-facing reporting, embedded analytics, or multi-tenant data models
- Experience with row-level security, access controls, or governed self-serve analytics
- Experience using Python for analysis, scripting, data validation, or automation
- Helped establish a semantic layer, metrics layer, or company-wide source of truth
- Built data models in a high-growth startup or operationally complex environment
- Experience improving warehouse performance, cost, and query efficiency
Responsibilities
- 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
Benefits
- 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 for all other parents
- HSA + FSA contributions
- Life insurance, plus short and long-term disability coverage
- Free daily lunch in-office
- Annual learning stipend
- Relocation assistance
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
