Analytics Engineer

Counsel Health•New York, NY

1d•$185,000 - $210,000•Hybrid

About The Position

Counsel's mission is to multiply the world's healthcare capacity by building the first fully-vertical, AI-native medical group. We are seeking an Analytics Engineer to own the semantic layer, building and maintaining core data models that the company relies on for insights. This is the first dedicated analytics hire, offering the opportunity to set standards, choose patterns, and shape the company's data strategy for years to come. The infrastructure is in place, and the company is growing, requiring someone to transform raw data into trusted models for dashboards, data products, and strategic decisions.

Requirements

3+ years of experience in analytics engineering, data analytics, or a hybrid data role
Deep expertise in SQL and dbt
Experience with BigQuery or comparable cloud data warehouses (Snowflake, Redshift)
Experience working in a regulated industry (healthcare, fintech, insurance, government) with a proven ability to manage sensitive data and access controls, HIPAA familiarity a plus
Experience with data orchestration tooling (Paradigm, dbt Cloud, Airflow, or similar) and integrating analytics data back into production systems

Nice To Haves

Thinks in business entities and stakeholder questions, not just tables and joins, and turns fuzzy requirements into clean, well-modeled data
Treats dbt as a primary tool, writing clean, modular, well-tested transformations, with strong opinions about how a dbt project should be organized
Comfortable managing orchestration, environments, and reverse ETL pipelines without hand-holding, even without being a dedicated data engineer
Doesn't just model data, but asks why someone needs it and how it drives a patient or business outcome
Feels energized by creating structure in ambiguous, fast-moving environments, and can synthesize needs from multiple stakeholders into a clear data strategy

Responsibilities

Build and maintain the data models that define our core business metrics, from clinical outcomes to revenue to utilization, and own the sources of truth the whole company relies on
Run dbt orchestration with integrated alerting, CI/CD pipelines, and data quality testing that catches issues before they reach a dashboard
Stand up reverse ETL pipelines that feed clean, modeled data back into production systems, including population cohort definitions, marketing intelligence, and operational features
Implement and maintain access controls for PHI, keeping the data layer HIPAA-compliant while staying accessible to the people who need it