Staff Data Analytics Engineer

Key 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.

Qualifications

• 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 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, entrepreneurial spirit, excellent communication, remote work adaptability, and commitment to continuous improvement.

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.