Azure Data Engineer (Azure)

TL;DR

Azure Data Engineer (Azure/Databricks): Developing and maintaining data ingestion pipelines from source systems to raw data layers with an accent on Azure data platforms and ETL/ELT processes. Focus on improving reliability, performance, and maintainability of data workflows within client-embedded teams.

Location: Must work from the European Union region and a work permit are required.

Company

Global AI-first digital transformation and engineering partner with over 25 years of experience and a team of 5,000 professionals.

What you will do

• Develop and maintain data ingestion pipelines from source systems to the raw data layer.
• Onboard new data sources into the data platform.
• Contribute to the ongoing development and improvement of the Azure-based data platform.
• Support and enhance monitoring and orchestration of data workflows.
• Participate as a fully embedded member of the client's internal data team.
• Identify and implement opportunities to improve pipeline reliability, performance, and maintainability.

Requirements

• Strong hands-on experience with Azure data platforms, including ingestion, storage, and processing.
• Solid understanding of data pipeline design, development, and maintenance.
• Practical experience with ETL/ELT processes and data integration patterns.
• Experience with Databricks and data workflow orchestration.
• English: Very good communication skills required.
• Location: Must be based in the European Union and possess a valid work permit.
• Experience using AI-powered assistants (e.g., Claude Code, GitHub Copilot, Cursor) to improve productivity.

Nice to have

• Previous experience in banking or financial services.
• Exposure to regulated environments and data governance practices.
• Experience working in distributed or nearshore teams.
• Experience applying GenAI in the SDLC, including defined workflows and prompt patterns.
• Interest in agent-based workflows and AI-augmented development.

Culture & Benefits

• Access to leading technologies including AWS, Azure, GCP, Databricks, and Snowflake.
• People-first culture with a strong focus on engineering excellence, growth, and knowledge sharing.
• Opportunity to work with high-profile global clients such as McLaren, Spotify, and Disney.
• Consulting mindset with an emphasis on continuous learning.

Hiring process

• CV review and HR call.
• Technical interview.
• Client interview.
• Final decision.