ML Data Engineer – Healthcare Data Curation & Cleaning
Listed on 2026-01-11
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IT/Tech
Data Engineer, Big Data
ML Data Engineer – Healthcare Data Curation & Cleaning (1 Year Fixed Term) at Stanford University summary:
The ML Data Engineer at Stanford University is responsible for designing, developing, and maintaining automated machine learning-accelerated data pipelines to curate, clean, and transform large-scale healthcare datasets. This role focuses on ensuring high-quality, standardized healthcare data compliant with industry models like OMOP CDM, supporting downstream machine learning and predictive analytics. The position requires collaboration with multidisciplinary teams to optimize data workflows and uphold healthcare data integrity and security in a research environment.
Stanford University is seeking a Big Data Architect 1 for a 1 year fixed term (possibility of renewal) to design and develop applications, test and build automation tools and support the development of Big Data architecture and analytical solutions.
About Us:
The Department of Biomedical Data Science merges the disciplines of biomedical informatics, biostatistics, computer science and advances in AI. The intersection of these disciplines is applied to precision health, leveraging data across the entire medical spectrum, including molecular, tissue, medical imaging, EHR, biosensory and population data.
About the Position:
We are seeking an experienced ML Data Engineer to drive the programmatic curation, cleaning, and generation of healthcare data. In this role, you will focus exclusively on developing and maintaining automated, ML-accelerated pipelines that ensure high-quality data ready for machine learning applications. Your work will be pivotal in shaping the integrity of our data and supporting downstream predictive models in a complex healthcare environment.
You Will Find This Position a Good Fit If:
● You are passionate about transforming raw healthcare data into valuable insights.
● You believe in the critical role of robust data curation in advancing machine learning in healthcare.
● You thrive in environments where you can work independently on complex data challenges while collaborating with multidisciplinary teams.
● You are excited to work with patient-level data and embrace challenges related to data diversity and complexity.
Duties include:
● Design Big Data systems that are scalable, optimized and fault-tolerant.
● Work closely with scientific staff, IT professional and project managers to understand their data requirements for existing and future projects involving Big Data.
● Develop, test, implement, and maintain database management applications. Optimize and tune the system, perform software review and maintenance to ensure that data design elements are reusable, repeatable and robust.
● Contribute to the development of guidelines, standards, and processes to ensure data quality, integrity and security of systems and data appropriate to risk.
● Participate in and/or contribute to setting strategy and standards through data architecture and implementation, leveraging Big Data, analytics tools and technologies.
● Work with IT and data owners to understand the types of data collected in various databases and data warehouses.
● Research and suggest new toolsets/methods to improve data ingestion, storage, and data access.
Key Responsibilities:
● Data Pipeline Engineering:
○ Design, implement, and maintain robust pipelines for the programmatic cleaning, transformation, and curation of healthcare data.
○ Develop automated processes to curate and validate data, ensuring accuracy and compliance with healthcare standards (e.g. OMOP CDM, FHIR).
● ML Data Engineering:
○ Leverage core machine learning techniques to generate datasets, clean existing health records, join heterogeneous data sources, and enhance data quality for model training.
○ Implement innovative solutions to detect and correct data inconsistencies and anomalies in large-scale healthcare datasets.
● Healthcare Data Expertise:
○ Work extensively with patient-level health data, ensuring that data handling practices adhere to industry regulations and ethical standards.
○ Utilize the OMOP Common Data Model (OMOP CDM) to standardize and harmonize disparate healthcare data sources,…
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