Senior Analytics Engineer

Join Sprinter as a Senior Analytics Engineer, where you'll build the trusted data layer that drives our operations and supports our health plan partners. You'll own the canonical models, metric definitions, transformation logic, documentation, and tests that ensure our data is reliable and reusable across the company. This role requires a deep understanding of data modeling, metric definitions, and collaboration with various teams. Enjoy benefits like free lunch, health insurance, unlimited PTO, and a flexible work-from-home policy.

• Construire des modèles de données canoniques qui créent une source de vérité partagée au sein de l'entreprise.
• Définir et maintenir les indicateurs clés de performance (KPI) de l'entreprise, opérationnels, financiers, produits et orientés client.
• Modéliser les données dans dbt ou un outil de transformation équivalent afin que les tableaux de bord, les analyses en libre-service et les rapports clients s'appuient sur des tables fiables.
• 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
• Balanced speed, correctness, usability, and maintainability when building data assets
• Defined, maintained, or reconciled business-critical metrics across teams
• Built tested, documented, reusable data models rather than one-off queries
• Debugged data quality issues, dashboard changes, metric discrepancies, and lineage problems
• Written expert-level SQL and designed data models that support reporting, analysis, and decision-making
• Communicated clearly with technical and non-technical stakeholders about what data means and how it should be used
• 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
• Worked with dbt or an equivalent transformation framework
• Partnered with analysts, data scientists, operators, finance teams, product teams, or customer-facing stakeholders
• What you have done:
• You’ve built data models in a high-growth startup or operationally complex environment
• You’ve worked with PHI, HIPAA-aware data access patterns, or other sensitive regulated data
• You’ve helped establish a semantic layer, metrics layer, or company-wide source of truth
• You have experience using Python for analysis, scripting, data validation, or automation
• You have experience with healthcare data, claims data, EHR data, payer data, provider data, or other complex healthcare datasets
• You have experience improving warehouse performance, cost, and query efficiency
• 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
• What gives you an edge: