Staff Data Analytics Engineer

Function Health•Canada, KS

8h•Remote

About The Position

Function Health is building the most comprehensive picture of human health ever assembled and making sense of that data is core to our mission. We're looking for a Staff Analytics Engineer to own the semantic layer that sits at the heart of our data platform: the trusted, well-modeled foundation that powers dashboards, self-serve analytics, and increasingly, AI-driven data agents. This isn't a greenfield "figure out what to build" role. We have a platform. Now we need someone to make it exceptional, clean models, a well-governed metrics layer, and the architecture to let AI query our data reliably and accurately. You'll collaborate closely with data scientists, product engineers, and business leaders to ensure that when anyone (or anything) asks a question about our data, they get the right answer. This is a fully remote role. Function Health is a distributed team, we hire across the US.

Requirements

8+ years of analytics engineering or data engineering experience, with clear progression to staff-level scope and impact.
Deep SQL expertise and strong Python skills.
Strong data modeling fundamentals — you know what clean, extensible, well-documented models look like and can build and enforce that standard regardless of tooling. Experience with dbt is a plus.
Experience with modern data stack tools: Databricks, Snowflake or BigQuery, Airflow or similar orchestration, Looker/Tableau or equivalent BI tools.
Track record of building resilient, monitored, production-grade data systems — not just pipelines that work, but ones that stay working.
Strong communicator who can make technical tradeoffs legible to non-technical stakeholders.
Bias Toward Action: Demonstrated ability to take initiative, make decisions under uncertainty, and move projects forward even in the face of ambiguity. We value individuals who are self-starters and ready to act on opportunities and challenges alike.
Entrepreneurial Spirit: Strong adaptability to changing business needs with a knack for building and optimizing processes. Your entrepreneurial mindset will be crucial in navigating the dynamic landscape of our industry, ensuring our platform remains competitive and responsive to user needs.
Communication: Excellent communication skills, capable of explaining complex technical concepts to non-technical stakeholders. Effective communication is vital for cross-functional collaboration and ensuring alignment across our organization.
Remote Work Adaptability: Comfort with remote work environments, demonstrating the ability to stay productive and connected with the team irrespective of physical location.
Continuous Improvement: A willingness to question assumptions and a commitment to continuous improvement. Your openness to feedback and dedication to personal and professional growth will contribute significantly to our collective success.

Nice To Haves

Experience with health tech or regulated data environments (HIPAA, PHI/PII handling).
Familiarity with Databricks Unity Catalog, Delta Lake, or Spark.
Experience building or integrating with AI/LLM-powered analytics tools or data agents.
GCP experience.

Responsibilities

Own the semantic layer: Design and maintain the metrics, dimensions, and data models that serve as the single source of truth across the business — built to be queried by humans and AI alike.
Architect for AI-readiness: Structure our Databricks and GCP-based data platform so it can reliably power AI-driven analytics agents — clean schemas in Unity Catalog, consistent naming conventions, and strong data contracts.
Drive data quality end-to-end: Build observability, testing, and alerting into pipelines so we catch problems before stakeholders do.
Enable self-serve analytics: Partner with product, clinical, and business teams to deliver tooling and dashboards that let them answer their own questions without filing tickets.
Set the standard: Establish and enforce data modeling conventions, documentation practices, and code review norms across the data org.
Collaborate cross-functionally: Translate ambiguous business questions into data solutions; work with product engineering to improve event logging and measurement coverage.