Data Engineer

About the Role

As Data Engineer, you will promote data as a key differentiator for Nomia. You will help drive innovation and the building of intelligent systems for internal use, as well as for our customers and suppliers globally. You will be a key member of the team devoted to designing and implementing cutting-edge Agentic solutions.

Roles & Responsibilities

• Design and implement data pipelines for cleaning and enriching data
• Be responsible for the data lake
• Find and curate third-party datasets
• Prepare data for use in experiments
• Design and maintain notebooks to measure the quality and completeness of data
• Understand real-world use cases and translate them into actionable plans
• Conduct experiments to validate design choices or theories
• Create, manage, monitor, and maintain data models
• Design and implement ETL processes
• Document all aspects of your work
• Stay abreast of advancements in data engineering and research new software and techniques
• Participate in code reviews, technical discussions, and cross-functional meetings

About You

• 3+ years' experience in data engineering
• Demonstrable experience with Microsoft Azure tools, such as Function Apps and services including Data Factory, Azure Synapse, and Azure Databricks
• Demonstrable experience designing and implementing ETL pipelines
• Proficient in PostgreSQL and T-SQL
• Proficient in Python, with a strong command of data processing libraries such as Pandas and PySpark
• Proficient in the use of Python notebooks
• Experience with event-driven architecture
• Familiarity with LLMs and prompt engineering
• Proficient in writing clean, maintainable code and well-documented data pipelines
• Wide knowledge of different database types and designs
• Familiarity with data modelling techniques
• Genuine enthusiasm for learning new ideas and techniques

General

• Ensure compliance with Nomia's data protection and information security policies
• Hybrid work model — 3 days per week in office, with flexibility based on training or team needs
• Promote inclusivity, innovation, and ethical use of AI across the organisation
• Be adaptable and proactive in learning new tools, techniques, and methods as the AI landscape evolves