- Compensation: EUR 38400 - EUR 55000 - yearly

Company DescriptionNIQ is a publicly traded company, the world’s leading consumer intelligence company, delivering the most complete understanding of consumer buying behavior and revealing new pathways to growth. In 2023, NIQ combined with GfK, bringing together the two industry leaders with unparalleled general reach. With a holistic retail read and the most comprehensive consumer insights, delivered with advanced analytics through state-of-the-art platforms. NIQ delivers the Full View (TM).

Inscríbase sin demora Se espera un gran volumen de solicitantes para el puesto que se detalla a continuación, no espere para enviar su CV.
We're looking for an experienced Data Engineer to join our growing Machine Learning team. You'll build and maintain the data infrastructure that powers our ML workflows, from raw data ingestion to model deployment.
Job DescriptionResponsibilities
- Design and build data pipelines/flows for ML use cases
- Deploy and manage flows in orchestration tools
- Improve and maintain CI/CD pipelines




- Deploy data scientists' scripts and models from notebooks to production
- Implement data quality checks to ensure pipeline reliability
- Optimize data processing jobs for performance and cost, particularly on distributed systems and cloud data services

QualificationsQualifications
- 3–5 years of experience as a Data Engineer
- Strong proficiency in Python
- Experience with distributed systems for big data processing (Dask, Spark)
- Experience building ETL pipelines
- Hands-on experience with GCP core data services (Cloud Storage, BigQuery, Cloud Build, GKE)
- Experience with orchestration tools (Airflow, Dagster, or similar)
- Comfortable working in a Linux environment with Jupyter, Python, Git, Docker, SQL, and NoSQL
- Strong experience with Pandas, SQL, and writing Dockerfiles

Nice to have
- Experience with ML datasets
- Experience building data pipelines for AI models
- Experience with LangGraph and RAG

📌 Data Engineer (Madrid)
🏢 NielsenIQ
📍 Madrid