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# Data and Platform Engineer

- **Company:** Synctiv
- **Location:** Brussel, Belgium
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, Microsoft Azure, Cloud Database, Computer Programming, Continuous Integration, Information Engineering, Digital Assets, Python (Programming Language), Machine Learning, Standard Sql, Apache Spark, Git, Containerization, Data Lakes, AI Platforms, Pyspark, Kubernetes, Machine Learning Operations, Terraform, Data Pipelines, Docker, Databricks
- **Published:** August 21, 2026
- **Apply:** https://www.careerjet.be/jobad/be361a39b7ea3c60cecf9c790983dafc9b

## About the Role

3+ years of experience in Data Engineering or Platform Engineering roles.
Strong experience with cloud-based data analytics environments.
Hands-on expertise with Databricks (Spark, Jobs, Workflows, Delta Lake).
Solid experience with Infrastructure-as-Code (Terraform preferred).
Strong programming skills in Python and PySpark.
Good knowledge of SQL, Git, and CI/CD practices.
Understanding of containerization concepts (Docker, Kubernetes is a plus).
Experience with cloud platforms (Azure is a plus).
Fluent in English.
Availability to work on-site 2 days per week.
What will make you successful
Experience building or supporting Data Science / AI platforms.
Strong problem-solving mindset and ability to work in collaborative, cross-functional teams.
Ability to communicate clearly with both technical and non-technical stakeholders.

## Description

We are looking for a Data & Platform Engineer to join an Advanced Analytics team building a next-generation Data Science and AI platform. You will play a key role in designing and maintaining the data foundations that enable analytics, machine learning, and AI solutions across the organisation.
This is a great opportunity for an engineer who enjoys building scalable data pipelines, enabling data teams, and contributing to a modern cloud-based data and AI ecosystem.
What you will do
Platform Engineering
Build and maintain a modern Data Science & AI platform supporting ML experimentation, deployment, and monitoring.
Implement MLOps practices and CI/CD pipelines for data and ML workloads.
Design scalable and reliable infrastructure for data and AI solutions.
Data Engineering
Develop and optimize robust data ingestion pipelines with a focus on reliability, quality, and security.
Build and maintain data models supporting analytics and advanced use cases.
Collaborate with Data Scientists to operationalize features, datasets, and ML workflows.
Collaboration & Enablement
Work closely with Data Scientists, Analysts, and Architects to deliver reusable, high-quality data assets.
Improve platform usability through documentation, automation, and continuous enhancements.


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