Data Consultant, Data Services

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Employer: SUSTAINALYTICS
Domain:
  • Quality Control
  • Management - Consulting
  • Job type: full-time
    Job level: 1 - 5 years of experience
    Location:
  • Timisoara
  • Updated at: 18.08.2022
    Remote work: On-site
    Short company description

    For more than 25 years, Sustainalytics has focused on delivering high-quality, analytical environmental, social and governance (ESG) research, ratings and data to institutional investors and companies.

    Our research is empowering world’s leading institutional investors to identify, understand, and manage risks and opportunities from an ESG perspective.

    Our Corporate Solutions unit works with hundreds of companies and their financial intermediaries to help them consider sustainability in policies, practices, and capital projects.

    To learn more information about us, please visit our website.

    Requirements

    Business Stakeholder Requirements:

    Bachelor’s/Master’s degree in statistics, informatics, economics or mathematics;
    Strong English skills;
    Relevant (2+ years) work experience in handling data and building reports;
    Strong analytical skills including data QA knowledge/experience;
    Solid Microsoft Excel skills;
    Python and/or SQL skills or commitment to learn;
    Willingness to learn new solutions and technologies (e.g. BI tools – Power BI);
    Adaptable, creative, autonomous, solution driven;
    Highly organized, meticulous, striving for excellence;
    Integrity and teamwork skills;
    Good understanding of corporate data, market data, fundamental data, and reference data is considered an asset.

    Recommended Action:

    Python and/or SQL skills

    Responsibilities

    Role & Responsibility:

    Create new client deliverables in accordance with procedures;
    Create/contribute to developing technical tools for automated quality assurance;
    Perform end of gate quality control on data;
    Advise on technical feasibility of new deliverable requests and create samples;
    Interpret data connecting content knowledge with technical knowledge;
    Monitor and report on team KPIs;
    Contribute to projects and product development;
    Identify opportunities for deliverable automation and process optimization.

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