Data Engineer — Hybrid, Cloud Data Pipelines & ML Ready

MobiLab Solutions; Aveiro; Híbrido; EUR 45 000 - 65 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Hybrid work mode

Informal and relaxed work environment

Lots of growth opportunities in an国际公司

Young, energetic, and dynamic team

Resumo da oferta

At MobiLab Solutions, we are looking for a Data Engineer who can shape data architectures and bring data-driven insights to life across teams.

You will design and maintain data lakes/warehouses, ingest multiple sources, enable ML models, and promote cloud-native data practices in an agile, cross-functional environment. You’ll collaborate, document thoroughly, and drive deployment of scalable data pipelines.

Qualificações

• 3+ years of experience as a Data Engineer.
• Bachelor's degree in computer science, data science, data engineering, mathematics or physics.
• Experience in semantic modeling of complex data landscapes (Data Lake, Data Warehouse, Data Vault, Data Mart).
• Strong knowledge of distributed filesystems, SQL and NoSQL stores.
• Ability to structure data pipelines for reliability, scalability, and performance.
• Experience with analytics engines like Spark or Flink.

Responsabilidades

• Design, develop, optimize, and maintain data architecture.
• Ingest data from multiple sources and maintain pipelines.
• Analyze, transform, and process large datasets.
• Enable training and running machine learning models.
• Help customers become cloud-native and data-driven.
• Support knowledge transfer and tool adoption.
• Contribute to new tools, methods, and techniques in an agile team.

Conhecimentos

Data engineering mindset

Python

Scala

SQL

Spark

Flink

Team player

Documentation

Formação académica

Bachelor's degree in CS / Data Science / Data Engineering

Ferramentas

JSON

Parquet

ORC

Descrição da oferta de emprego

At MobiLab Solutions, we are looking for a Data Engineer who can shape data architectures and bring data-driven insights to life across teams.

You will design and maintain data lakes/warehouses, ingest multiple sources, enable ML models, and promote cloud-native data practices in an agile, cross-functional environment. You’ll collaborate, document thoroughly, and drive deployment of scalable data pipelines.