Staff Data Engineer

Confirmed live in the last 24 hours

Interwell Health

Remote, United States

Hybrid

Posted April 8, 2026

Job Description

Interwell Health is a kidney care management company that partners with physicians on its mission to reimagine healthcare—with the expertise, scale, compassion, and vision to set the standard for the industry and help patients live their best lives. We are on a mission to help people and we know the work we do changes their lives. If there is a better way, we will create it. So, if our mission speaks to you, join us!

Reporting to the Director of Data Engineering, the Staff Data Engineer serves as a senior technical leader responsible for shaping, scaling, and governing our modern data ecosystem. This role blends architecture, hands-on engineering, platform leadership, and cross functional partnerships to deliver high quality data products that power clinical, operational, financial, and analytical outcomes. Deep experience with Databricks, Python, dbt, and Microsoft Fabric, along with strong fluency in healthcare data and compliance standards, is essential. At its core, you’ll work closely with teams across the organization to deliver governed, high‑quality, analytics‑ready data at scale.

Our Tech Stack: Databricks, Delta Lake, Unity Catalog, Microsoft Fabric (OneLake, Lakehouse, Data Factory), Azure, dbt, Python, PySpark, Spark SQL.

What You’ll Do:

Architecture & Strategy

Design and evolve a scalable, secure, cloud‑native lakehouse platform leveraging Databricks, Microsoft Fabric (OneLake, Lakehouse, Data Factory), and dbt.
Define modeling patterns, governance frameworks, and engineering best practices across the data lifecycle.
Lead design reviews and guide teams in adopting scalable architectural patterns.
Drive long‑term platform strategy and evaluate emerging technologies.

Hands-on Engineering

Design and implement batch and streaming data pipelines for healthcare data sources (EHR, claims, HL7/FHIR, APIs, flat files, databases)
Develop modular ingestion, quality, lineage, metadata, and observability frameworks that scale across domains.
Produce clean, analytics‑ready datasets and data models for BI, analytics, and machine learning workloads.