Data Engineer: Scalable Pipelines & ML Production

Inetum; Porto; Presencial; EUR 42 000 - 66 000; Tempo integral

Resumo da oferta

Inetum is seeking a Mid-Level Data Engineer to design, optimize, and maintain scalable data pipelines, ensuring reliability and performance of data solutions. You will collaborate with Data Science teams to integrate and operationalize ML models in production environments.

The role emphasizes building robust data processing, optimizing workflows for large datasets, and contributing to best practices in software development within a multinational digital services leader.

Qualificações

• 3+ years of experience in Data Engineering.
• Advanced proficiency in Python and SQL.
• Demonstrated ability to design robust, maintainable, and highly scalable solutions for large-scale data processing.
• Hands-on experience with in-memory data processing libraries such as Polars (or equivalent).
• Strong knowledge of software development best practices and version control.
• Experience deploying, monitoring, and maintaining ML pipelines in production environments.

Responsabilidades

• Design, develop, and maintain scalable data pipelines.
• Optimize data processing workflows for large-scale datasets.
• Build and manage data ingestion solutions from multiple sources.
• Ensure data quality, reliability, and performance across platforms.
• Collaborate with Data Science and Engineering teams to deploy and operationalize ML solutions.
• Contribute to software engineering best practices and continuous improvement.

Conhecimentos

Python

SQL

Data pipelines

PySpark

Polars

Distributed processing

Git

CI/CD

ML production

Ferramentas

Databricks

Apache Airflow

AWS

Azure

GCP

Descrição da oferta de emprego

Inetum is seeking a Mid-Level Data Engineer to design, optimize, and maintain scalable data pipelines, ensuring reliability and performance of data solutions. You will collaborate with Data Science teams to integrate and operationalize ML models in production environments.

The role emphasizes building robust data processing, optimizing workflows for large datasets, and contributing to best practices in software development within a multinational digital services leader.