Data Engineer

Kantar Media is a global leader in research focused on understanding how people think, feel, shop, and consume media. Operating in more than 100 countries, the company helps clients gain deeper insights into media audiences and their relationships with brands, enabling smarter and more effective investment decisions.

About the Role

We are seeking a skilled and proactive Data Engineer to join our dynamic, international team. In this role, you will architect, and maintain scalable data solutions that power our business intelligence and operational systems. You will play a key part in advancing our organization’s Lakehouse, running on Azure Databricks platform, and strengthening the core of our Quality Control (QC) framework for data related to TV viewing, internet streaming, and browsing. You will work across data integration, cloud architecture, database optimization, and ETL/ELT development, collaborating closely with cross‑functional stakeholders such as analysts, data scientists, and client directors. Most of your time will be spent partnering with DevOps engineers to bridge the gap between Dutch market requirements and standardized data processing practices.

To thrive in this position, you must confidently diagnose issues in production processes by analyzing source data—whether it originates from third‑party online tagging providers or from Kantar Media’s own devices that measure internet usage and TV viewing. Therefore, the ideal candidate combines strong technical expertise with critical thinking skills, ownership mindset, and the ability to operate effectively in both local and international environments.

Key Responsibilities Design, develop, test, and maintain robust, scalable data pipelines to ensure reliable data integration across systems. Develop and maintain advanced Python-based data processing frameworks and automation tools to meet evolving business requirements. Architect, develop, and optimize complex ETL/ELT pipelines supporting analytics, reporting, and operational use cases. Enforce best practices for data integration, transformation, orchestration, and monitoring while optimizing database systems (MS SQL, Oracle, MySQL) and cloud platforms (Azure Data Factory, Azure Databricks) to ensure performance, scalability, and reliability. Monitor and troubleshoot data workflows, ensuring data quality, consistency, and integrity. Develop reporting and visualization solutions using Power BI or similar BI tools. Perform reverse engineering of existing software applications to understand data structures, scripts, and system logic when necessary. Stay up to date with emerging data engineering technologies and best practices, contributing to continuous improvement initiatives.

Qualifications & Skills Bachelor’s or Master’s degree in Computer Science, Information Technology, Data Science, Mathematics, Statistics, or related fields. Fluency in English Good understanding of data warehousing concepts and modern data lake/lakehouse architectures. Strong SQL expertise, including query optimization, indexing strategies, and database design principles. Solid Python programming skills for data transformation, automation, and integration tasks. Hands-on experience with Azure cloud services, particularly Azure Data Factory and Azure Databricks. Experience working with big data technologies such as Spark and workflow orchestration tools (e.g., Airflow). Proven experience designing and maintaining production-grade ETL/ELT pipelines. Strong analytical and troubleshooting skills with a structured problem-solving approach. Excellent communication skills with the ability to explain complex technical concepts to non-technical stakeholders. Self-driven, adaptable, and capable of taking ownership of end-to-end data solutions.