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Data Engineer

Skills

aianalyticsawsazureclouddata engineeringdata modelingdata pipelinesdata qualitydbtfinopsgcpobservabilityroot cause analysissqlSQL

Description

Overview

In this role, you will build and optimize scalable data pipelines and models to deliver analytics-ready data that enables data-driven decision-making. You’ll work within the Data Platform & AI Team to design batch and streaming pipelines, support BI with high-performance data models, and implement data quality and observability to maintain reliability. You’ll translate business needs into scalable data solutions and drive continuous improvement in data incidents. This position offers a chance to impact data-driven decisions across the organization while operating in a cloud-centric, FinOps-minded environment.

Retribuzione / Benefits
  • €40,000–€50,000 gross salary
  • corporate welfare services
  • equal opportunity employer
Responsabilità
  • Design, develop, and maintain scalable batch and streaming data pipelines for ingestion, transformation, and delivery
  • Collaborate with BI teams to deliver data models, data marts, and semantic layers for business needs
  • Implement data quality, monitoring, and observability frameworks to ensure data reliability
  • Manage data incidents with root cause analysis and continuous improvement to prevent recurrence
  • Translate business and analytical requirements into scalable, maintainable data solutions
Requisiti fondamentali
  • Minimum 5 years of professional data engineering experience
  • Strong SQL skills
  • Experience building transformation layers from raw data to analytics-ready datasets
  • Experience with dbt or similar analytics engineering tools
  • Cloud experience with at least one major provider (GCP, Azure, or AWS)
  • Performance tuning, partitioning, pruning, and cost-efficient cloud usage (FinOps mindset)
  • Clear communicator with both technical and non-technical stakeholders
  • Curious and proactive problem-solver
  • dbt or similar analytics engineering tools
  • SQL proficiency
  • Batch and streaming data pipeline development
  • Data modeling for analytics-ready datasets

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