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    Data Engineer - Montreal, Canada - Research Institute of the McGill University Health Centre

    Research Institute of the McGill University Health Centre
    Research Institute of the McGill University Health Centre Montreal, Canada

    1 week ago

    Default job background
    Full time
    Description

    Do you want to work for a world-renowned research institute that pushes the boundaries of biomedical science and health research? Right here in Montreal At the Research Institute of the McGill University Health Centre (RI-MUHC), you can be part of an organization focused on scientific discovery and innovation in patient-centered medicine. Join us today and make a difference

    Job Description

    RESEARCH INSTITUTE OF THE MUHC
    The Research Institute of the McGill University Health Centre (RI-MUHC) is a world-renowned biomedical and hospital research centre. Located in Montreal, Quebec, the Institute is the research arm of the McGill University Health Centre (MUHC) affiliated with the Faculty of Medicine at McGill University. The RI-MUHC is supported in part by the Fonds de recherche du Québec - Santé (FRQS).

    Position summary
    Under the direct supervision of the Director of Digital Health Research the incumbent is part of a team building the data backbone for a learning health system at the MUHC and digital health research at the RI-MUHC.

    The data engineer is responsible for architecting, implementing, and maintaining compute frameworks, analysis tooling, and/or model implementations used or created by the Data Science team to support the management and analysis of clinical and administrative data at the McGill University Health Centre and the RI-MUHC.

    The goal is to ensure the creation and ongoing operation of a modern research data analytics ecosystem for use by the MUHC and RI-MUHC communities. This ecosystem will concurrently facilitate research and clinical performance and quality operations, including dashboard-based tracking and quality assessment.

    General Duties

    •Build end-to-end data pipelines and infrastructure for statistical and ML models used by the Unit and others at the MUHC,

    •Understand the requirements of data processing and analysis pipelines and make appropriate technical design and interface decisions. Elucidating these requirements will require understanding researcher-built or vendor provided machine learning algorithms applied to hospital data and working with other members of the Data Science team,

    •Contribute to development of a Data Lake for real-time clinical data in HL7 format and the creation of data models in formats such as FHIR and APIs to allow access to clinical data for the development of machine learning models,

    •Understand data flows among the MUHC applications and use this knowledge to make recommendations and design decisions for languages, tools, and platforms used in software and data projects,

    •Troubleshoot and debug environment and infrastructure problems found in production and non-production environments for projects by the Data Science team,

    •Work with other groups at the MUHC to ensure servers and system maintenance based on updates, system requirements, data usage, and security requirements.

    Website of the organization

    Education / Experience


    •Bachelor's or Master's degree in Computer Science, Engineering, or related, or equivalent working experience,

    •3-5 years of experience in building data infrastructure for analytics teams, including ability to write code in SQL, R, or Python for processing large datasets in distributed cloud environments.

    Required Skills,

    •Experience with cloud deployment strategies and CI/CD,

    •Experience building and working with data infrastructure in IaaS and SaaS environments,

    •Experience overseeing, developing or implementing machine learning operations (MLOps) processes,

    •Experience working with healthcare data, including HL7 and FHIR formats. Experience with other standards (, LOINC, SNOMED, ICD-10) and data models (, OMOP CDM) is an asset,

    •Knowledge of multiple programming languages, commitment to choosing languages based on project-specific requirements, and willingness to learn new programming languages as necessary,

    •Knowledge of resource management and automation approaches such as workflow runners,

    •Collaborative mentality and excitement for iterative design working closely with the Data Science team.

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