Data Analyst
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Employer: | SUSTAINALYTICS |
Domain: |
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Job type: | full-time |
Job level: | 1 - 5 years of experience |
Location: |
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Updated at: | 26.08.2021 |
Remote work: | On-site |
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.
The team is responsible to construct client deliverables by aggregating, evaluating and analysing data and to provide context on the content. The team also performs end of gate quality control on the data by means of change detection and validation. In its operations, the team makes use of smart tools and technologies to perform their tasks.
Qualifications
Bachelor’s/Master’s degree in statistics, informatics, economics or mathematics;
Relevant (2+ years) work experience in handling data and building reports;
Strong analytical skills including data QA knowledge/experience;
Python and/or SQL skills or commitment to learn;
Solid Microsoft Excel skills;
Willingness to learn new solutions and technologies (e.g. BI tools – Power BI);
Strong English skills;
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.
Job Responsibilities
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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