
Workday Data Analyst
- Buenos Aires
- Permanente
- Tiempo completo
As a Data Analyst (Associate) in Kainos, you’ll be responsible for matching the needs of data insight with understanding of the available data. Data analysts work closely with customers to produce insight products including reports, dashboards and visualisations but also contribute to project understanding of existing data structures so that inputs and outputs are fully understood.
Most of our work comes through repeat business and direct referrals, which comes down to the quality of our people. The success of our Data Engineering teams means that customers are bringing us an increasing number of exciting data projects using cutting-edge technology to solve real-world problems. We are seeking more high calibre people to join our Data & Analytics capability where you will grow and contribute to industry-leading technical expertise.
MINIMUM (ESSENTIAL) REQUIREMENTS:
- Experience of data gathering, data manipulation, data discovery and data profiling
- Able to understand the client’s business challenge and recommend data visualisation and dashboard approaches to help address the customer needs. Able to identify missed opportunities for data insight
- Sound understanding of data model and modelling techniques
- Clear written and verbal communications; able to communicate with a wide range of people; understands the importance of stakeholder engagement and is able to gather requirements
- Familiar with the production of data analysis outputs such as profiling reports, data quality reports, etc.
- Proficient in more than one reporting or data visualisation platform.
- Strong SQL knowledge; able to read and understand XML and JSON.
- Able to advise customers and managers on the estimated effort and technical implications of data insight products.
- Able to review and comment on data models
- Experienced with structured and unstructured data
- Experience of Tableau and Google Analytics
- Experience in combining qualitative and quantitative datasets
- Experience of system performance analysis