Data Engineer - ML Pipelines & Cloud

Nabla; Madrid; Presencial; EUR 45.000 - 80.000; Jornada completa

Descripción de la vacante

NIQ is seeking an experienced Data Engineer to join our Machine Learning team in Madrid. You will build and maintain the data infrastructure powering ML workflows, from raw data ingestion to model deployment, and collaborate with data scientists to push notebooks to production.

Key responsibilities include designing pipelines, implementing data versioning, and managing orchestration tools like Airflow. Strong Python and Linux skills, plus experience with GCP and Docker, are required.

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.
• Experience building reliable, maintainable data 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.
• Professional‑level English.

Responsabilidades

• Design and build data pipelines/flows for ML use cases.
• Implement data versioning practices.
• Deploy and manage flows in orchestration tools.
• Improve and maintain CI/CD pipelines.
• Manage Python dependencies (Poetry, uv).
• 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

Distributed systems

ETL pipelines

Pandas

GCP

Airflow

SQL

Docker

Git

Kubernetes

English

Herramientas

Docker

Airflow

Dagster

Git

Kubernetes

Descripción del empleo

NIQ is seeking an experienced Data Engineer to join our Machine Learning team in Madrid. You will build and maintain the data infrastructure powering ML workflows, from raw data ingestion to model deployment, and collaborate with data scientists to push notebooks to production.

Key responsibilities include designing pipelines, implementing data versioning, and managing orchestration tools like Airflow. Strong Python and Linux skills, plus experience with GCP and Docker, are required.