Senior/Principal Data Engineer with strong MLOps expertise

Drivers of change, it’s your time to pave new ways. Intellias, a leading software provider in the automotive industry, invites you to shape the future of driving. Join the team and co-create digital products for the world’s top-tier brands.

Project Overview:

• Our customer is a Dutch multinational developer and creator of location technology and consumer electronics headquartered in Amsterdam. They are looking for a skilled Data Engineer with strong MLOps experience to lead the design and automation of production-grade data and ML pipelines on Azure Databricks. You’ll work at the intersection of data engineering, infrastructure automation, and machine learning — transforming prototype workflows into robust, observable, and cost-efficient pipelines.

Requirements:

• Strong proficiency in Python is essential, along with experience in shell scripting and potentially other languages like Java.
• Hands-on experience with at least one major cloud service provider (Azure is preferable)
• Experience in Azure Databricks Workflows, Delta Lake, and Unity Catalog
• Experience in PySpark, SQL Warehouses, and Databricks Asset Bundles (DAB) under CI/CD (GitHub Actions or Azure DevOps).
• Strong software engineering practices, including testing, code optimization, and design patterns.
• Excellent communication and collaboration skills to bridge the gap between technical and non-technical teams.

Responsibilities:

• Design and maintain end-to-end data and ML pipelines using Databricks Workflows, Delta Lake, and Unity Catalog (bronze–silver–gold layers, schema evolution, access policies).
• Build reproducible training and deployment workflows integrated with tools for experiment tracking, model registry, and artifact management.
• Implement data quality frameworks and observability metrics aligned with industry best practices.
• Build and monitor dashboards (e.g. past experience with Lakeview, Grafana, or similar) for data quality, model performance, and operational metrics.
• Automate data ingestion and feature generation jobs, leveraging PySpark, SQL Warehouses, and Databricks Asset Bundles (DAB) under CI/CD (GitHub Actions or Azure DevOps).
• Manage access and security, ensuring compliance and reliability.
• Optimize compute performance and cost (spot/autoscaling, cluster tuning, caching, partitioning).