AI-Augmented Data Engineer
Summary
Build and maintain AI-augmented data pipelines and scalable data products, modeling data for analytics, BI, and self-service use cases in a cloud environment.
Overview
- Design, build, and maintain robust data pipelines
- Model data for analytics, BI, AI, and self-service use cases
- Build Bronze, Silver, Gold, Semantic Layer, and Exposition Layer data models
- Transform complex business problems into simple, durable, and scalable data products
- Contribute to reactivity, freshness, and near-real-time data needs
- Implement a complete Tech Flow based on an “as-code everywhere” approach
- Improve the quality, performance, governance, and observability of data pipelines
Requirements
- At least 4 years of solid experience in a demanding data, analytics, BI engineering, or software engineering environment with a strong data focus
- Strong experience with modern Data Engineering or Analytics Engineering practices
- Technically curious, creative, and constantly learning
- Demand for quality, performance, scalability, and maintainability
- Ability to transform unclear business needs into clear and reliable data solutions
- Comfortable working in autonomous squads and cloud environments
- Enjoy automation, knowledge sharing, and continuous improvement
- Understand the value of product-oriented data engineering
- Excited by AI-augmented ways of working and already use AI tools to improve productivity and delivery quality
- Fluent in English
Hard Skills
- Data Engineering
- Business Intelligence
- Data Transformation
- Data Governance
- Data Performance Optimization
- Scalable Data Products
- Tech Flow Implementation
- Data Observability
- Self-Service Analytics
- Product-Oriented Data Engineering
Soft Skills
- Technical Curiosity
- Creativity
- Continuous Improvement
- Knowledge Sharing
- Autonomous Work