Analytics Engineer
Build and maintain canonical data models, metric definitions, and dbt transformations to create a trusted, reusable data layer for internal teams and payer customers in a healthcare startup. Requires expert SQL, dbt experience, metric reconciliation, and a product-oriented approach to data quality and documentation.
About the job
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
- Experience with healthcare data, claims data, EHR data, payer data, provider data, or other complex healthcare datasets
- Worked with PHI, HIPAA-aware data access patterns, or other sensitive regulated data
- Experience building customer-facing reporting, embedded analytics, or multi-tenant data models
- Worked 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
What makes you successful
- Treat a metric definition as a product artifact, not a Slack thread
- Make data trustworthy, reusable, and easy to understand
- Prevent metric chaos by building clear definitions, tests, and documentation
- Build so that a fix in one place does not require five copy-paste edits elsewhere
- Understand that internal users and external customers both need data they can trust
- Care about the usability of the data model, not just whether the pipeline runs
- Can explain data discrepancies clearly and drive teams toward shared definitions
- Build foundations that help the company move faster with more confidence
Day to Day
- 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
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 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
Skills
SQL, dbt, BigQuery, Snowflake, Redshift, Databricks, Python, Data Modeling, Metrics Definition, Data Lineage, Data Testing, HIPAA, Phi
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