Data Engineer: ELT Pipelines on AWS & Data Platform

Mentor Talent Acquisition; Catania; In loco; EUR 70.000 - 110.000; Tempo pieno

Vantaggi offerti da questo lavoro

Remote options

International team

Descrizione del lavoro

Mentor Talent Acquisition is seeking a Data Engineer to design and operate scalable ELT pipelines powering analytics and core features. You will work with Python, SQL and AWS while focusing on data quality, performance and maintainability.

Responsibilities include building ELT pipelines, implementing AWS-based data workflows, and contributing to our Data Platform architecture. The role emphasizes collaboration with engineering and product teams across a modern data stack.

Competenze

• Strong Python skills for data processing and ETL/ELT pipelines.
• Proficient in SQL and dimensional data modeling.
• Hands-on AWS experience with data services and data quality practices.

Mansioni

• Design, build and maintain scalable ELT pipelines.
• Implement data workflows using AWS services (Lambda, Step Functions, S3).
• Contribute to Data Platform architecture evolution (Medallion).
• Ensure data quality, consistency, and reliability across pipelines.
• Optimize transformations for performance and cost.
• Collaborate with Backend/Frontend and Product to define data requirements.
• Monitor pipelines, troubleshoot, and improve observability.
• Document data models, transformations, and architectural decisions.

Conoscenze

Python

ETL/ELT pipelines

SQL

Medallion Architecture

Data quality

Git & CI/CD

AWS (data services)

Strumenti

AWS

Docker

Terraform

ClickHouse/BigQuery/Redshift

Descrizione del lavoro

Mentor Talent Acquisition is seeking a Data Engineer to design and operate scalable ELT pipelines powering analytics and core features. You will work with Python, SQL and AWS while focusing on data quality, performance and maintainability.

Responsibilities include building ELT pipelines, implementing AWS-based data workflows, and contributing to our Data Platform architecture. The role emphasizes collaboration with engineering and product teams across a modern data stack.