Hybrid Data Engineer - Big Data Pipelines (Spark/AWS)

IRIUM Portugal

Lisboa

Hybrid

EUR 45,000 - 75,000

Full time

19 hours ago
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Benefits offered by this job

Flexible pay according to experience
Medical insurance as social benefit
Access to technological training

Job summary

IRIUM Portugal is seeking a Data Engineer (Spoke) to join Lisbon-based hybrid team. You will upload data to the Big Data Platform, prepare data for use cases, and build ready-to-use data pipelines, collaborating with the service provider for complex tasks.

We expect 4 years of experience, English C1, and strong knowledge of Spark, Python, AWS ingestion, and distributed systems. You will work with Docker deployments, CI/CD pipelines, and agile Scrum processes in a dynamic automotive-related data

Qualifications

  • 4 years experience in data engineering or equivalent.
  • English level C1.
  • Proficient in Spark, Python, R, Scala, Java and Kotlin.
  • Understanding of AWS Cloud Ingestion solutions and S3/databases.
  • Knowledge of distributed systems and data pipelines.
  • Experience with Docker-based deployment and resource management.
  • Familiarity with Scrum/agile software development.

Responsibilities

  • Uploading data to the Big Data Platform.
  • Targeted preparation of data for specific use cases and data profiling.
  • Development of ready-to-use data pipelines.
  • Coordination of the service provider's activities for complex tasks.

Skills

Spark
Python
R
Scala
Java
Kotlin
Docker
CI/CD
Workflow Managers
Agile / Scrum
AWS Cloud Ingestion

Tools

CI/CD tools

Job description

IRIUM Portugal is seeking a Data Engineer (Spoke) to join Lisbon-based hybrid team. You will upload data to the Big Data Platform, prepare data for use cases, and build ready-to-use data pipelines, collaborating with the service provider for complex tasks.

We expect 4 years of experience, English C1, and strong knowledge of Spark, Python, AWS ingestion, and distributed systems. You will work with Docker deployments, CI/CD pipelines, and agile Scrum processes in a dynamic automotive-related data

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