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    Data Engineer - London, United Kingdom - 83DATA

    83DATA
    83DATA London, United Kingdom

    5 days ago

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    Description
    Data Engineer/ScientistLondon (Hybrid)£50,000-£60,000 (+bens)83DATA are partnered with a leading Internet provider seeking the skills of a Data Engineer/Scientist. The successful candidate can expect to be tasked to connect data sources to pre-existing Azure data infrastructure.

    You will lead projects with a data centric approach, for this reason, a passion and aptitude for critical analysis of large and complex datasets is vital.


    Responsibilities:


    Build data engineering pipelines to ingest, transform, store, and serve high quality data for Data ScientistsWriting and tuning complex data pipelines, workflows, and data structures.

    Identify opportunities for increasing performance, minimising costs and increase platform monitoring.
    Experience with components such as Azure Active Directory, Management Groups, Subscriptions, vNets, Route Tables, NSG's and Azure Firewalls.
    Ensuring data security and maintaining quality assurance across all systems.
    Promote a data driven culture, advocating for the use of data and analytics in decision making and process optimisation.
    Using Predictive modelling solutions for current data and generating a model to help predict future outcomes.


    Requirements:
    A Computer Science/Programming Degree.
    Background of using data analysis tools and software (e.g., SQL, SAS, Python and R). SQL – Advanced ProficiencyExperienced in Machine Learning and predictive modelling.
    Background working with consultancy services.
    Highly experienced with MS Azure cloud platforms. 3 years minimum.
    Understanding of building resilient multi-site architectures.
    Proven ability to develop and execute user retention strategies via CRM and product.

    Experience of large complex data migrationsProven experience in cost optimisation and performance efficiencyProven experience in Data Science and/or Machine Learning including an understanding of fundamental technologies and processes involved in the research and development of data science models.

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