Remote Data Engineer for AI Biomedical Platform

Washington University; St. Louis, Northern (MO, KY); Hybrid; USD 120,000 - 180,000; Full time

Job summary

Washington University in St. Louis is seeking a C-BRAIN Data Engineer to design, build, and maintain data infrastructure powering AI biomedical research tools.

You will manage ingestion, harmonization, and cloud infrastructure for multi-modal datasets across NACC, ADNI, and consortium data contributions. The role emphasizes production-grade engineering, cloud governance, and reproducible data workflows, collaborating with CTO, PMM, and data science teams.

Qualifications

• Bachelor's degree in Computer Science, Data Science, Bioinformatics, Engineering, or a closely related field.

Responsibilities

• Designs, builds, tests, and maintains scalable data ingestion pipelines to ingest consortium member datasets into the C-BRAIN data infrastructure.
• Develops and maintains ETL/ELT workflows using tools such as Apache Spark, dbt, Airflow, or equivalent; ensure pipelines are robust, well-documented, and auditable.
• Implements automated pipeline monitoring and alerting; troubleshoot and resolves pipeline failures in a timely manner.
• Maintains version control for all pipeline code and infrastructure configurations; follows software engineering best practices including code review and documentation.
• Integrates and processes multi-modal data including omics, neuroimaging, longitudinal clinical records, and digital pathology; reconciles differences into unified analytical frameworks.

Skills

Python

SQL

Azure

AWS/GCP

Git

Airflow

dbt

Spark

Education

Bachelor's degree in Computer Science or related field

Master’s or PhD preferred

Tools

Git

Airflow

dbt

Apache Spark

Job description

Washington University in St. Louis is seeking a C-BRAIN Data Engineer to design, build, and maintain data infrastructure powering AI biomedical research tools.

You will manage ingestion, harmonization, and cloud infrastructure for multi-modal datasets across NACC, ADNI, and consortium data contributions. The role emphasizes production-grade engineering, cloud governance, and reproducible data workflows, collaborating with CTO, PMM, and data science teams.