Data Engineer - Barcelona

ISG Personalmanagement
Barcelona, Spain
2 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Intermediate

Job location

Barcelona, Spain

Tech stack

Amazon Web Services (AWS)
Azure
Big Data
Information Engineering
Data Integration
ETL
Hadoop
Python
SQL Databases
Unstructured Data
Google Cloud Platform
Data Storage Technologies
Spark
Database Performance
Information Technology
Kafka
Data Pipelines

Job description

We are looking for a Data Engineer with experience in designing, building, and maintaining scalable data pipelines and architectures. This role focuses on ensuring that data is reliable, accessible, and ready for analysis, supporting strategic initiatives and data-driven decision-making across the organization. Responsibilities:

  • Design, develop, and maintain ETL/ELT pipelines for structured and unstructured data.
  • Collaborate with data scientists, analysts, and business teams to understand data requirements.
  • Ensure data quality, integrity, and consistency across multiple sources.
  • Optimize database performance and manage large-scale data storage solutions.
  • Implement data integration solutions on cloud and on-premise platforms.
  • Monitor, troubleshoot, and improve data processes and workflows.

Requirements

  • Bachelor's degree in Computer Science, Engineering, Data Science, or related fields.
  • 3-5 years of experience in data engineering, database management, or similar roles.
  • Proficiency in Python, SQL, and ETL tools or data pipeline frameworks.
  • Familiarity with cloud platforms (AWS, Azure, Google Cloud) and big data technologies (Spark, Hadoop, Kafka, etc.).
  • Strong analytical skills, attention to detail, and ability to work collaboratively across teams.

Benefits & conditions

  • Participation in strategic projects with direct impact on business outcomes.
  • A collaborative, multidisciplinary environment with opportunities for professional growth.
  • Implementation of best practices in data engineering and architecture.

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