Data Engineer – Passenger Flow & Platform Services 260901-JAS-001

ITproposal; Antwerpen; On-site; EUR 70,000 - 120,000; Full time

Job summary

ITproposal in Belgium is seeking an experienced Backend & Data Platform Engineer to build, scale, and maintain the real-time passenger timeslot services and capacity balancing systems. You will work closely with product owners, data scientists, and fellow engineers to deliver production-grade solutions.

You will develop Python backend services and REST APIs, construct reliable data pipelines, and deploy to Azure with Kubernetes/OpenShift.

Responsibilities

• Build, scale, and maintain core data platform, backend apps, and real-time REST APIs.
• Bridge between product owners, data scientists, and engineering teams for reliable solutions.
• Develop robust Python backend services for internal and external use.
• Create data pipelines and integrations to support real-time workflows.
• Deploy and monitor apps in Azure and Kubernetes/OpenShift production environments.
• Drive high availability, resilience, and rapid incident response.
• Operationalize and scale machine learning models and data science products into production.

Job description

• Role Objective: Build, scale, and maintain the core data platform, backend applications, and REST APIs for real-time passenger timeslot services and operational capacity balancing. Act as a bridge between product owners, data scientists, and engineering teams to deliver reliable, production-grade solutions.
• Backend & API Engineering: Develop and maintain robust Python backend services and REST APIs for internal and external consumption.
• Data Pipelines & Integrations: Construct reliable data pipelines and integrations to support real-time operational workflows and capacity products.
• Cloud & Operations: Deploy, manage, and monitor applications in Azure and Kubernetes/OpenShift production environments.
• Reliability & Incident Management: Drive high availability, platform resilience, continuous improvements, and rapid troubleshooting/incident response.
• Data Science Productionization: Operationalize and scale machine learning models and data science products into production.
• Engineering Standards: Enforce CI/CD pipelines, automated testing, clean code practices, and modern software engineering standards.