Analytics Engineer

Counsel Health•New York, NY

About The Position

At Counsel Health, we've built a strong data foundation — a cloud warehouse, ETL pipelines, and event tracking are all in place. Now we need someone to make that data mean something. We're looking for an Analytics Engineer who will own the semantic layer of our data, building and maintaining the core analytics data models that the rest of the company will use to turn raw data into insights. You will be the person the company trusts to answer "how do we measure this?” The infrastructure is built. The data warehouse is running. The company is growing. What's missing is the person who makes the data layer actually work — who turns raw tables into the trusted models that power every dashboard, every data product, and every strategic decision. You'll be the first dedicated analytics hire, which means you define the standards, choose the patterns, and shape how Counsel Health uses data for years to come.

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 proven ability to manage sensitive data and access controls.

Nice To Haves

HIPAA familiarity is a plus.
Experience with data orchestration tooling (Paradigm, dbt Cloud, Airflow, or similar), and integrating analytics data back into production systems

Responsibilities

Own the Semantic Layer: Build and maintain data models that define our core business metrics — from clinical outcomes to revenue to utilization. You will own the sources of truth for Counsels core metrics and ensure they can be reliably used throughout the company.
Orchestrate & Monitor: Run dbt orchestration with integrated alerting, CI/CD pipelines, and data quality testing to catch issues before they reach dashboards.
Build Reverse ETL Pipelines: Stand up the data flows that feed clean, modeled data back into production systems — population cohort definitions, marketing intelligence, and operational features that depend on well-modeled warehouse data.
Own Data Compliance: Implement and maintain access controls for PHI data, ensuring the data layer meets HIPAA requirements while remaining accessible.