Data Analytics Engineer

Uni Systems

Brussels, Belgium

Description

At Uni Systems, we are working towards turning digital visions into reality. We are continuously growing and we are looking for a Data Analytics Engineer to join our UniQue team in Brussels!

What will you be doing in this role:

Process and analyze CSV files as primary data input, ensuring data accuracy, completeness, and integrity;
Develop, deploy, and maintain interactive Shiny dashboards that use the CSV files as data input and allow to visualize data insights effectively;
Develop R scripts for data cleaning, manipulation, and analysis;
Develop Tableau dashboards to visualize data;
Work closely with stakeholders to understand requirements, provide data-driven insights, and improve dashboard functionality;
Continuously optimize and update Shiny applications to enhance performance and user experience;
Document data processes, analysis workflows, and dashboard functionalities for future reference and team collaboration;
Write the different documentation associated with the tasks and liaise with other project teams as necessary to address cross-project interdependencies;
All these activities require interactions with business stakeholders and with the IT teams, as well as a strong participation in business and/or functional working groups, progress meetings and meetings with the stakeholders (IT and business).

Requirements

What do you need to succeed in this position?

A Masters degree + 13 years of relevant professional experience;
Excellent knowledge of data analytics techniques and tools;
Expertise in the ETL processes;
Knowledge of R-Studio;
Strong command of R for data manipulation, analysis, and visualization. Experience with R packages such as dplyr, ggplot2, readr, and data.table;
Experience building interactive dashboards using R Shiny (or similar), including familiarity with shiny libraries, reactive programming, and UI/UX best practices within Shiny;
Experience in handling, cleaning, and processing data from CSV (comma-separated values) files, including experience with large datasets and optimizing CSV file handling in R (or Python);
Experience in data wrangling, statistical analysis, and creating clear, insightful visualizations using R (or Python) and Shiny (or similar);
Experience in developing R functions;
Experience using Git or other version control systems for collaborative work;
A good command of the English language.