Azure Data Engineer

Requirements

• You have a very good knowledge of ETL tools and create data processing pipelines
• You can develop data infrastructure diagrams
• You create and document data models
• You can work with Fabric and Azure Data Factory
• You know containerization (Docker) and orchestration (Kubernetes)
• You write scripts freely in Python or Bash
• You have experience working with cloud infrastructure
• You understand what MLOps is and how to manage the ML model lifecycle
• You work well in a team – both with technologists and analysts

Duties

• You design, implement, and maintain data processing/transformation pipelines
• You build and automate environments for training and deploying ML models – from experiments to monitoring
• You manage cloud infrastructure (mainly Azure, but AWS or GCP is also welcome) and containerization (Docker, Kubernetes)
• You implement and maintain tools for model, data, and code versioning (e.g., MLflow, DVC)
• You monitor system performance and respond to incidents when necessary
• You collaborate with Data Science, ML, and Software Engineering teams to improve deployment processes

Technology Stack

• Fabric
• Azure Data Factory
• Docker
• Kubernetes

Data Science

At the Data Science team at Univio, we believe that data is the foundation of success in modern business.

Our mission is to support retail companies in their transformation into data-driven organizations, enabling them to fully leverage the potential of their collected information.

We take pride not only in delivering advanced technologies, but most importantly in providing comprehensive solutions to real business problems faced by our clients.

We specialize in key areas related to data and artificial intelligence:

• Data Consulting: We advise clients on data strategy and identify opportunities for leveraging their data.
• Data Science: We conduct advanced data analyses, identify trends and patterns, and draw insights.
• Data Engineering: We design and implement scalable and reliable data pipelines.
• Machine Learning & AI: We build ML models, LLMs, AI assistants, and automate processes.
• Business Intelligence: We design and implement reporting and data visualization systems.