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Data Solution Developer

Plný úvazek
Etterbeek
Plat: Neuvedeno
Žádná práce na dálku
zkušenosti: > 3 roky
Vzdělání: 3 rok VŠ

dFakto
dFakto

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Pozice

Popis pozice

Presentation of the position
We are looking for someone to analyze and implement our data warehouses. You will help our Data team to design solutions based on our Agile Data Warehouse Automation Platform. As member of dFakto, you will help our project team. You will help analyze client business requirements, design their business model and create a data vault schema and the information mart. You will design solutions who transform data into information relevant to business. The goal of you and your teammates will be to create Agile Data Warehouse Automation Platforms that can restitute relevant and consistent information that can answer different departments, teams or hierarchy levels needs within the client organization. The scope of skills, technologies and industries you’ll get to explore is huge and the team will be there to support you on your way.

Tasks
On a day to day basis, you will go on learning and contribute to the deliverables of the project by carrying out complex and varied activities including, typically:

  • Designing high-quality solutions with the right balance between coupling and cohesion principles
  • Designing high-performing solutions
  • Bringing technical expertise required to extract data from multiple sources systems
  • Identifying potential issues and suggesting original solutions

Požadavky na pozici

Profile

  • Bachelor or master’s degree in computer science or 3-5 years of experience in Data Analysis
  • Functional Analysis skills to develop requirements and specifications
  • Database Design on one or multiple RDBMS (PostgreSQL, SQLServer, Oracle, MySQL/MariaDB, Redshift,…)
  • Very good knowledge of SQL
  • Data extract, transformation and loading (ETL) methodology from source systems to data warehouses
  • Speak English & French fluently

Nice to have Qualifications

  • Knowledge of Data Vault modeling is a big plus.
  • OLAP principals such as dimensional analysis, star schemas, aggregation and query creation
  • Master Data Management, Data Quality

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