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