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Big Data Engineer

job details

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    job details
    Skills Required
    o Data Organization & Retrieval
    o Data Classification & Cataloguing
    o Data versioning tools and strategies
    o Design, construct and maintain large-scale data processing systems. This collects
    data from various data sources -- structured or not.
    o Evaluate, compare and improve data pipelines. This includes design pattern
    innovation, data lifecycle design, data ontology alignment, annotated data sets and
    elastic search approaches.
    o Prepare automated data pipelines to transform and feed the data into dev, QA and
    production environments.
    o Partner with technical and non-technical colleagues to understand data and reports
    requirements
    o Knowledge of Geospatial Datasets is a plus
    o Knowledge of Cloud Architecture is a plus
    o Knowledge of Database’s technologies (Postgre, SQL) is a plus
    o Knowledge of Backup & Archival strategies and technologies is a plus
    o Knowledge of Data Science and Machine Learning is a plus

     Main responsibilities
    1. Create a data organization & retrieval strategy for large data sets containing
    georeferenced data, images, videos, files and databases
    2. Create the classification and catalog of current and future data
    3. Support the different business units requiring data & information. For example,
    collaborate with the software engineering team to provide relevant information to
    the Operation team to answer RFPs in an accurate way.
    4. Support the IT team to elaborate the most efficient backup and archival strategies
    5. Acquire and manage new datasets based the needs of the different business units
    6. Manage Data annotation for the data science team using third-party providers
    7. Load and maintain data in operational software.

    Advantages
    Skills Required
    o Data Organization & Retrieval
    o Data Classification & Cataloguing
    o Data versioning tools and strategies
    o Design, construct and maintain large-scale data processing systems. This collects
    data from various data sources -- structured or not.
    o Evaluate, compare and improve data pipelines. This includes design pattern
    innovation, data lifecycle design, data ontology alignment, annotated data sets and
    elastic search approaches.
    o Prepare automated data pipelines to transform and feed the data into dev, QA and
    production environments.
    o Partner with technical and non-technical colleagues to understand data and reports
    requirements
    o Knowledge of Geospatial Datasets is a plus
    o Knowledge of Cloud Architecture is a plus
    o Knowledge of Database’s technologies (Postgre, SQL) is a plus
    o Knowledge of Backup & Archival strategies and technologies is a plus
    o Knowledge of Data Science and Machine Learning is a plus

    Responsibilities
    1. Create a data organization & retrieval strategy for large data sets containing
    georeferenced data, images, videos, files and databases
    2. Create the classification and catalog of current and future data
    3. Support the different business units requiring data & information. For example,
    collaborate with the software engineering team to provide relevant information to
    the Operation team to answer RFPs in an accurate way.
    4. Support the IT team to elaborate the most efficient backup and archival strategies
    5. Acquire and manage new datasets based the needs of the different business units
    6. Manage Data annotation for the data science team using third-party providers
    7. Load and maintain data in operational software.

    Qualifications
    Skills Required
    o Data Organization & Retrieval
    o Data Classification & Cataloguing
    o Data versioning tools and strategies
    o Design, construct and maintain large-scale data processing systems. This collects
    data from various data sources -- structured or not.
    o Evaluate, compare and improve data pipelines. This includes design pattern
    innovation, data lifecycle design, data ontology alignment, annotated data sets and
    elastic search approaches.
    o Prepare automated data pipelines to transform and feed the data into dev, QA and
    production environments.
    o Partner with technical and non-technical colleagues to understand data and reports
    requirements
    o Knowledge of Geospatial Datasets is a plus
    o Knowledge of Cloud Architecture is a plus
    o Knowledge of Database’s technologies (Postgre, SQL) is a plus
    o Knowledge of Backup & Archival strategies and technologies is a plus
    o Knowledge of Data Science and Machine Learning is a plus

    Summary
    Skills Required
    o Data Organization & Retrieval
    o Data Classification & Cataloguing
    o Data versioning tools and strategies
    o Design, construct and maintain large-scale data processing systems. This collects
    data from various data sources -- structured or not.
    o Evaluate, compare and improve data pipelines. This includes design pattern
    innovation, data lifecycle design, data ontology alignment, annotated data sets and
    elastic search approaches.
    o Prepare automated data pipelines to transform and feed the data into dev, QA and
    production environments.
    o Partner with technical and non-technical colleagues to understand data and reports
    requirements
    o Knowledge of Geospatial Datasets is a plus
    o Knowledge of Cloud Architecture is a plus
    o Knowledge of Database’s technologies (Postgre, SQL) is a plus
    o Knowledge of Backup & Archival strategies and technologies is a plus
    o Knowledge of Data Science and Machine Learning is a plus

    Randstad Canada is committed to fostering a workforce reflective of all peoples of Canada. As a result, we are committed to developing and implementing strategies to increase the equity, diversity and inclusion within the workplace by examining our internal policies, practices, and systems throughout the entire lifecycle of our workforce, including its recruitment, retention and advancement for all employees. In addition to our deep commitment to respecting human rights, we are dedicated to positive actions to affect change to ensure everyone has full participation in the workforce free from any barriers, systemic or otherwise, especially equity-seeking groups who are usually underrepresented in Canada's workforce, including those who identify as women or non-binary/gender non-conforming; Indigenous or Aboriginal Peoples; persons with disabilities (visible or invisible) and; members of visible minorities, racialized groups and the LGBTQ2+ community.

    Randstad Canada is committed to creating and maintaining an inclusive and accessible workplace for all its candidates and employees by supporting their accessibility and accommodation needs throughout the employment lifecycle. We ask that all job applications please identify any accommodation requirements by sending an email to accessibility@randstad.ca to ensure their ability to fully participate in the interview process.
    Skills Required
    o Data Organization & Retrieval
    o Data Classification & Cataloguing
    o Data versioning tools and strategies
    o Design, construct and maintain large-scale data processing systems. This collects
    data from various data sources -- structured or not.
    o Evaluate, compare and improve data pipelines. This includes design pattern
    innovation, data lifecycle design, data ontology alignment, annotated data sets and
    elastic search approaches.
    o Prepare automated data pipelines to transform and feed the data into dev, QA and
    production environments.
    o Partner with technical and non-technical colleagues to understand data and reports
    requirements
    o Knowledge of Geospatial Datasets is a plus
    o Knowledge of Cloud Architecture is a plus
    o Knowledge of Database’s technologies (Postgre, SQL) is a plus
    o Knowledge of Backup & Archival strategies and technologies is a plus
    o Knowledge of Data Science and Machine Learning is a plus

     Main responsibilities
    1. Create a data organization & retrieval strategy for large data sets containing
    georeferenced data, images, videos, files and databases
    2. Create the classification and catalog of current and future data
    3. Support the different business units requiring data & information. For example,
    collaborate with the software engineering team to provide relevant information to
    the Operation team to answer RFPs in an accurate way.
    4. Support the IT team to elaborate the most efficient backup and archival strategies
    5. Acquire and manage new datasets based the needs of the different business units
    6. Manage Data annotation for the data science team using third-party providers
    7. Load and maintain data in operational software.

    Advantages
    Skills Required
    o Data Organization & Retrieval
    o Data Classification & Cataloguing
    o Data versioning tools and strategies
    o Design, construct and maintain large-scale data processing systems. This collects
    data from various data sources -- structured or not.
    o Evaluate, compare and improve data pipelines. This includes design pattern
    innovation, data lifecycle design, data ontology alignment, annotated data sets and
    elastic search approaches.
    o Prepare automated data pipelines to transform and feed the data into dev, QA and
    production environments.
    o Partner with technical and non-technical colleagues to understand data and reports
    requirements
    o Knowledge of Geospatial Datasets is a plus
    o Knowledge of Cloud Architecture is a plus
    o Knowledge of Database’s technologies (Postgre, SQL) is a plus
    o Knowledge of Backup & Archival strategies and technologies is a plus
    o Knowledge of Data Science and Machine Learning is a plus

    Responsibilities
    1. Create a data organization & retrieval strategy for large data sets containing
    georeferenced data, images, videos, files and databases
    2. Create the classification and catalog of current and future data
    3. Support the different business units requiring data & information. For example,
    collaborate with the software engineering team to provide relevant information to
    the Operation team to answer RFPs in an accurate way.
    4. Support the IT team to elaborate the most efficient backup and archival strategies
    5. Acquire and manage new datasets based the needs of the different business units
    6. Manage Data annotation for the data science team using third-party providers
    7. Load and maintain data in operational software.

    Qualifications
    Skills Required
    o Data Organization & Retrieval
    o Data Classification & Cataloguing
    o Data versioning tools and strategies
    o Design, construct and maintain large-scale data processing systems. This collects
    data from various data sources -- structured or not.
    o Evaluate, compare and improve data pipelines. This includes design pattern
    innovation, data lifecycle design, data ontology alignment, annotated data sets and
    elastic search approaches.
    o Prepare automated data pipelines to transform and feed the data into dev, QA and
    production environments.
    o Partner with technical and non-technical colleagues to understand data and reports
    requirements
    o Knowledge of Geospatial Datasets is a plus
    o Knowledge of Cloud Architecture is a plus
    o Knowledge of Database’s technologies (Postgre, SQL) is a plus
    o Knowledge of Backup & Archival strategies and technologies is a plus
    o Knowledge of Data Science and Machine Learning is a plus

    Summary
    Skills Required
    o Data Organization & Retrieval
    o Data Classification & Cataloguing
    o Data versioning tools and strategies
    o Design, construct and maintain large-scale data processing systems. This collects
    data from various data sources -- structured or not.
    o Evaluate, compare and improve data pipelines. This includes design pattern
    innovation, data lifecycle design, data ontology alignment, annotated data sets and
    elastic search approaches.
    o Prepare automated data pipelines to transform and feed the data into dev, QA and
    production environments.
    o Partner with technical and non-technical colleagues to understand data and reports
    requirements
    o Knowledge of Geospatial Datasets is a plus
    o Knowledge of Cloud Architecture is a plus
    o Knowledge of Database’s technologies (Postgre, SQL) is a plus
    o Knowledge of Backup & Archival strategies and technologies is a plus
    o Knowledge of Data Science and Machine Learning is a plus

    Randstad Canada is committed to fostering a workforce reflective of all peoples of Canada. As a result, we are committed to developing and implementing strategies to increase the equity, diversity and inclusion within the workplace by examining our internal policies, practices, and systems throughout the entire lifecycle of our workforce, including its recruitment, retention and advancement for all employees. In addition to our deep commitment to respecting human rights, we are dedicated to positive actions to affect change to ensure everyone has full participation in the workforce free from any barriers, systemic or otherwise, especially equity-seeking groups who are usually underrepresented in Canada's workforce, including those who identify as women or non-binary/gender non-conforming; Indigenous or Aboriginal Peoples; persons with disabilities (visible or invisible) and; members of visible minorities, racialized groups and the LGBTQ2+ community.

    Randstad Canada is committed to creating and maintaining an inclusive and accessible workplace for all its candidates and employees by supporting their accessibility and accommodation needs throughout the employment lifecycle. We ask that all job applications please identify any accommodation requirements by sending an email to accessibility@randstad.ca to ensure their ability to fully participate in the interview process.