For 1 of our clients we are looking for a Data Engineer:

Role: 
Aggregate data from various sources in a central data store (data product) and create dashboarding on top for reporting & monitoring. This should satisfy the requirements of both management and technical teams and will in the future expand to gaining insights out of the data (with support from data scientists). 
Create a uniform data layer with a set of connectors for the project that can be reused by the various agents and systems employed within Synthetix.  Data sources range from Databricks to SharePoint and APIs. 
Support in the development of pipelines (DevOps)  

Tasks: 

• Identify the data sourced needed, create connections and aggregate the required data into a central repository 
• Support in the selection of data storage, transformation and visualisation tools 
• Create PowerBI (and other) dashboard to provide insights into the system and data 
• Create and maintain a uniform data layer with various connectors, providing a single way to access data, manage authentication / authorization and maintain connectors 

Profile: 
Required: 

• A minimum of 5 years of experience developing software systems at an enterprise scale and a minimum of 3 years with data driven applications  
• Azure (e.g. Data Factory) 
• Databricks ELT flows 
• Proficiency in Python and data transformation and analysis libraries (e.g. pandas) 
• Experience with writing connectors to external sources in an enterprise setting and creating a uniform data layer (e.g. integration hub) 
• Ability to work in a team, but also manage own tasks independently  
• Dashboarding experience (e.g. PowerBI, Grafana) 

Optional: 

• Scripting languages such as Powershell 
• Experience with GenAI / LLM related projects 
• Familiar with Azure DevOps 

 
Duration: Till end of 2025 + potential continuation in 2026 
Location: Hybrid (3 days/week in Olen) 
Language: Proficient in English, with Dutch being a plus 
Project: Synthetix is a GenAI-powered platform designed to support advanced reasoning and data retrieval across multiple data sources. It enables expert users to ask complex questions and receive structured, traceable answers by orchestrating AI agents that interact with internal systems.