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Company Description
At Devoteam, we believe that technology with strong human values can actively drive change for the better. Discover how Tech for People unlocks the future, creating a positive impact on the people and the world around us. We are a global leading player in Digital Transformation for leading organisations across EMEA, with a revenue of €1B. We believe in transforming technology to create value for our clients, partners and employees in a world where technology is developed for people. We are proud of the culture we have built together. We are proud of our people at the service of technology. We are proud of our diverse environment. Because we are #TechforPeople. Join our multidisciplinary team of Cloud experts, Designers, Business consultants, Security experts, Engineers, Developers and other extraordinary talents, spread across more than 20 EMEA countries. Become one of our tech and business leaders on cloud, data and cyber security. Let’s fuse creativity with technology together and build innovative solutions that actively change things for the better.
Job Description
Design the data model and data architecture to meet qualitative requirements; Model business requirements including data streams, integrations (Schema API, CRUD API); Design and apply reference data architectures in accordance with the requirements of the data management strategy; Design and apply a blueprint for an organizational data framework that defines how data is obtained, stored, consumed, integrated and managed in order to enrich the company's Data Lakes, MDUs and Data Marts.
Qualifications
Bachelor’s degree in the IT area or equivalent; Over 10 years' experience; Knowledge of methodology and carrying out architectural design surveys of analytical systems (Databases, Database replicas, Datalakes, and other storage systems, ETL, CDC, Spark transformations); Experience in designing Data Efficiency programs to reduce data duplication, simplify database replicas, simplify processes that materialize data in intermediate steps, increase the use of virtualization techniques, use of hot/cold/archive, methodologies for finding unused data (through lineage techniques), automatic pruning, reducing the resolution of time series in older periods, among others.