AWS Data Engineer — Cloud ETL & Data Lakes | Flexible Hours

Making Science Group S.A.; España; Hybrid; EUR 55,000 - 75,000; Full time

Benefits offered by this job

Indefinite contract

Competitive fixed salary + performance

Flexible payment plan

Ambassadors Program

Flexible hours

One remote day per week

Training & language classes

24 vacation days + holidays

Job summary

Making Science is seeking a data engineer to design and implement cloud-based data architectures on AWS, building scalable ETL/ELT pipelines and managing data lakes and warehouses. You will collaborate with analysts, scientists, and DevOps across international teams, contributing to data governance and security.

The role offers a hybrid work model with opportunities for growth and hands-on training. You will leverage AWS Glue, S3, Redshift, DynamoDB, and Athena, while advancing data modeling,

Qualifications

• Experience with AWS data and compute services (AWS Glue, S3, Lambda, Step Functions, Redshift, DynamoDB, Athena).
• Proficiency in Python or another major programming language.
• Understanding of data lake, data warehouse, and cloud-native data architectures.
• Experience with Infrastructure as Code (Terraform or CloudFormation).
• Familiarity with CI/CD pipelines, DevOps practices, monitoring, and observability (CloudWatch).
• Knowledge of data governance, security best practices, IAM, and compliance requirements.
• Strong communication skills in English for working with international teams and customers.
• Ability to collaborate with technical and non-technical stakeholders.
• Experience with Agile methodologies.
• Analytical mindset and problem-solving.

Responsibilities

• Design and implement cloud-based data architectures on AWS.
• Build, maintain, and optimize ETL/ELT pipelines using AWS Glue, Lambda, Step Functions, and related services.
• Develop and manage data lakes and data warehouses using S3, Redshift, DynamoDB, and Athena.
• Create and maintain data models and schemas for analytics, reporting, and operational use cases.
• Ensure data quality through validation, monitoring, and automated error-handling.
• Optimize data solutions for performance, reliability, and cost efficiency.
• Collaborate with data analysts, data scientists, DevOps, and other engineering teams.
• Participate in Agile ceremonies, helping estimate tasks, identify risks, and plan technical work.
• Troubleshoot data issues and support customer-facing teams with technical insights.
• Document architectural decisions, data workflows, and implementation guidelines.

Skills

AWS data services

Python

Data architectures

CI/CD

DevOps

CloudWatch

IAM & security

Agile

English communication

Data governance

Tools

Terraform

CloudFormation

AWS tools

Monitoring (CloudWatch)

VCS/CI tools

Job description

Making Science is seeking a data engineer to design and implement cloud-based data architectures on AWS, building scalable ETL/ELT pipelines and managing data lakes and warehouses. You will collaborate with analysts, scientists, and DevOps across international teams, contributing to data governance and security.

The role offers a hybrid work model with opportunities for growth and hands-on training. You will leverage AWS Glue, S3, Redshift, DynamoDB, and Athena, while advancing data modeling,