Sr. Data Engineer - Healthcare Data Infrastructure (Texas)

Qualified Health PBC•Houston, TX

5d•Hybrid

About The Position

Qualified Health is seeking a Data Engineer to support partner integration. This role focuses on building data pipelines that directly power AI systems used by clinicians across major U.S. health systems. These systems help doctors identify diagnoses, flag patients needing intervention, and improve outcomes. The Data Engineer will work in a pod with a Director and 1-2 peers. There is a clear growth path from Senior Data Engineer to Director of a pod. This role is onsite in Houston, TX 3 days/week.

Requirements

4+ years as a data engineer building production ETL/ELT pipelines.
Strong SQL skills (complex joins, window functions, CTEs across large healthcare datasets).
Proficiency with Python and PySpark for data transformation at scale.
Experience with cloud data platforms (Databricks preferred; Snowflake, BigQuery, or Redshift accepted).
Familiarity with FHIR data models or willingness to learn healthcare data standards quickly.
Azure cloud services experience (ADLS Gen2, Key Vault, networking basics).
Ability to work onsite in Houston, TX 3 days/week.
Bachelor's degree in Computer Science, Engineering, Data Science, Mathematics, or related technical field.

Nice To Haves

Epic Clarity/Caboodle experience or healthcare data background.
Delta Lake, Delta Sharing, or lakehouse architecture experience.
HL7 message processing or real-time healthcare data pipelines.
Experience working in a multi-tenant SaaS environment.

Responsibilities

Build and maintain data ingestion pipelines that pull from Epic Clarity databases, FHIR APIs, and HL7 feeds into our Databricks lakehouse architecture.
Execute data quality validation and mapping verification for each new workflow deployment.
Support 2-4 health system partners as part of a dedicated integration pod, working closely with your Director and directly with customer data teams.
Develop and optimize SQL queries against complex healthcare data models (hundreds of tables, billions of rows) including clinical, billing, and operational data.
Contribute to defining reusable data transformation patterns that accelerate future partner onboarding.