ML Data Engineer — Cloud Data Pipelines & ETL

Nielseniq-; Madrid; Presencial; EUR 38.000 - 55.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Flexible working environment

Volunteer time off

Descripción de la vacante

NIQ is seeking an experienced Data Engineer to join our Machine Learning team in Madrid. You will build and maintain data infrastructure powering ML workflows from ingestion to deployment, partnering with data scientists and engineers.

Responsibilities include designing data pipelines, deploying flows with Airflow, optimizing CI/CD, and ensuring data quality on cloud platforms like GCP. Ideal candidates have 3–5 years of data engineering, Python proficiency, and experience with Spark/Dask,

Formación

• 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.

Responsabilidades

• 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

Conocimientos

Python

Big data systems

ETL pipelines

GCP

Airflow

Docker

SQL

NoSQL

Pandas

Linux

Herramientas

Airflow

Docker

BigQuery

Cloud Storage

GKE

Descripción del empleo

NIQ is seeking an experienced Data Engineer to join our Machine Learning team in Madrid. You will build and maintain data infrastructure powering ML workflows from ingestion to deployment, partnering with data scientists and engineers.

Responsibilities include designing data pipelines, deploying flows with Airflow, optimizing CI/CD, and ensuring data quality on cloud platforms like GCP. Ideal candidates have 3–5 years of data engineering, Python proficiency, and experience with Spark/Dask,