Staff Associate II

The Department of Biostatistics at the Mailman School of Public Health, Columbia University, is seeking a full-time Staff Associate II.

The primary function of this position is to provide data engineering support for the department's and TRAIL4Health's research programs. The role develops and maintains secure, HIPAA-compliant data pipelines and computing environments on AWS, prepares large research datasets (including EHR and public health data), and ensures data quality, accessibility, and reproducibility for statistical and AI/ML project.

Responsibilities and duties:

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  Builds and maintains HIPAA-compliant AWS data pipelines; oversees data ingestion, cleaning, transformation, and documentation to support statistical and AI/ML research.
  
  
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  Troubleshoots data quality issues, system failures, and workflow bottlenecks; identifies efficient solutions for handling large or complex datasets; optimizes data processes to meet evolving research needs.
  
  
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  Independently selects data engineering methods and tools, prioritizes tasks, and implements best practices for secure, reproducible computing.
  
  
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  Implements and maintains cloud infrastructure, ETL pipelines, and databases; supports large-scale EHR/public health data and develops tools for automated workflows.
  
  
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  Collaborates with faculty, researchers, and students to understand data needs; provides guidance on data access and pipeline usage; prepares documentation and communicates technical updates clearly to the research team.
  
  

Minimum educational requirements for this position: Bachelor's degree in Biostatistics, Statistics, or related field.

Professional work experience for this position: 4 years recommended

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  Proficiency in Python, SQL, and ETL pipeline development. Experience with AWS cloud services and secure, HIPAA-compliant data handling. Ability to manage and process large research datasets (e.g., EHR, public health data)
  
  

Columbia University is an Equal Opportunity Employer / Disability / Veteran

Pay Transparency Disclosure

The salary of the finalist selected for this role will be set based on a variety of factors, including but not limited to departmental budgets, qualifications, experience, education, licenses, specialty, and training. The above hiring range represents the University's good faith and reasonable estimate of the range of possible compensation at the time of posting.