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# Data Engineer

- **Company:** CLdN Cargo
- **Location:** Belgium
- **Contract:** Permanent contract
- **Skills:** 3d Models, Agile Methodology, Microsoft Azure, Cloud Computing, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Data Vault Modeling, Data Warehousing, Python (Programming Language), Azure DevOps Pipelines, SQL Databases, Data Streaming, Azure Data Factory, GitHub Copilot, Data Delivery, Terraform, Data Pipelines, Databricks
- **Published:** September 22, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=d2110e31dd805d46

## About the Role

* Strong technical expertise in data engineering and Azure data services, including Data Factory, Databricks and Event Hub, combined with hands-on experience in Terraform, Azure DevOps pipelines, Python, SQL and dbt. You are able to translate complex data landscapes into scalable and maintainable solutions.

 * Strong data modelling capabilities, with knowledge of methodologies such as Inmon, Kimball and Data Vault, and the ability to design models that balance business requirements with architectural best practices.

 * Hands-on delivery mindset, combining strong engineering expertise with a structured and pragmatic approach. You take ownership from design through implementation and deployment and consistently deliver reliable solutions on time.

 * Experience working in Agile environments, actively contributing to planning, refinement, prioritisation and delivery while collaborating closely with both technical and business stakeholders.

 * Product and roadmap awareness, with the ability to contribute to roadmap definition, technical prioritisation and backlog decisions based on business value, technical dependencies and long-term platform needs.

 * Analytical and conceptual thinker, able to understand end-to-end data flows, identify dependencies and long-term implications, and make well-founded technical decisions.

 * Comfortable using AI-assisted development tools, such as GitHub Copilot and Claude Code, to support development, improve efficiency and accelerate engineering workflows while maintaining engineering quality and control.

 * Clear and adaptable communicator, able to explain technical concepts to both technical and non-technical audiences and build effective working relationships across teams.

 * Proactive problem solver, anticipating issues, driving automation and maintaining high standards for data quality, documentation, maintainability and operational stability.

## Description

* Data Pipeline Ownership: Take end-to-end responsibility for extracting, cleansing, transforming and delivering data into our data warehouse and medallion architecture, covering both batch and streaming pipelines.

 * ETL & Automation: Build, automate and optimise ETL processes to ensure reliable, efficient and scalable data flows across the organisation.

 * Azure Platform Engineering: Set up, maintain and continuously improve our Azure-based data platform, including Databricks, Data Factory, Event Hub and Azure DevOps pipelines, with a strong focus on stability, scalability and performance.

 * Infrastructure as Code: Manage and automate cloud infrastructure using Terraform, ensuring consistent, secure and repeatable deployments.

 * Release & Deployment Management: Execute and coordinate deployments across development, acceptance and production environments using Azure DevOps.

 * Data Modelling & Contracts: Design business-oriented data models, maintain data contracts and ensure the data warehouse supports both operational and analytical requirements.

 * Quality & Monitoring: Safeguard data quality across pipelines, proactively identify issues and ensure reliable and stable data delivery.

 * Roadmap & Prioritization: Contribute to the technical roadmap and backlog by helping prioritize initiatives based on business value, technical dependencies, platform needs and long-term maintainability.

 * Cross-functional Collaboration: Work closely with data analysts, functional analysts, developers, application owners and other stakeholders to translate business needs into robust and scalable data solutions.

 * Documentation & Standards: Document pipelines, data models and platform components in line with development, architectural and engineering standards.