Remote Staff Analytics Engineer: AI‑Ready Data & Semantics

Function Health

United States

Remote

USD 160,000 - 230,000

Full time

14 days+

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Job summary

Function Health is hiring a Staff Analytics Engineer to own the semantic layer at the heart of our data platform. You will ensure trusted metrics, well-governed data models, and reliable AI-ready analytics across teams.

You will collaborate with data scientists, product engineers, and business leaders to deliver scalable, self-serve analytics and high-quality data products that drive decisions and outcomes in a fully remote US-based environment.

Qualifications

  • 8+ years of analytics engineering or data engineering experience with staff-level scope.
  • Deep SQL expertise and strong Python skills.
  • Strong data modeling fundamentals with clean, extensible models; dbt a plus.
  • Experience with modern data stack tools: Databricks, Snowflake/BigQuery, Airflow, Looker/Tableau.

Responsibilities

  • Own the semantic layer: design and maintain metrics, dimensions, and models as single source of truth.
  • Architect for AI-readiness: enable reliable AI-driven analytics with clean schemas and data contracts.
  • Drive data quality end-to-end with observability, testing, and alerts.
  • Enable self-serve analytics by partnering with product, clinical, and business teams.
  • Set data modeling conventions, documentation, and code review standards.
  • Collaborate cross-functionally to translate business questions into data solutions.

Skills

SQL
Python
Data modeling
dbt
Databricks
Looker/Tableau

Tools

Databricks
Snowflake
BigQuery
Airflow
Unity Catalog

Job description

Function Health is hiring a Staff Analytics Engineer to own the semantic layer at the heart of our data platform. You will ensure trusted metrics, well-governed data models, and reliable AI-ready analytics across teams.

You will collaborate with data scientists, product engineers, and business leaders to deliver scalable, self-serve analytics and high-quality data products that drive decisions and outcomes in a fully remote US-based environment.

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