Azure Data Engineer

Skills:

• 2 to 4 years of hands-on experience as a Data Engineer in cloud environments
• Strong SQL skills
• Practical experience with Python in data engineering use cases
• Experience with Azure Data Factory and Databricks
• Good understanding of data pipelines, ETL/ELT and data lifecycle
• Ability to communicate clearly with both technical and non-technical stakeholders
• Experience working in Agile teams
• Cost awareness when designing data solutions
• Curiosity and willingness to learn continuously

Tech stack you will use:

• SQL for data processing and analytics
• Python for transformations and data quality logic
• Azure Data Factory for ingestion (1-to-1 pipelines)
• Databricks for transformations and data quality implementation
• Azure-based data platform services
• Git or similar source code management tools
• CLI environments (bash, PowerShell)

What's in it for you?

• Exciting role at a leading European bank
• 6 months assignment, B2B contract (with possibility to extend)
• Fully remote role with availability within Central European Time (CET)
• Personal growth and development opportunities

One of Europe’s banks is undergoing a digital transformation, focusing on cloud technologies and a customer-first approach. The goal is to unlock the full potential of enterprise data using scalable data engineering methods and strong data governance, including quality, metadata, and core management practices.

The Data Office works closely with IT and business teams to build a modern data platform and maintain data pipelines across both cloud and on-prem environments. Data will be a key driver in understanding and meeting customer needs.

We’re looking for someone who gets the fundamentals of Data Engineering - Data Management, DataOps, Security, Architecture, Orchestration and Software Engineering - and knows how to apply them with a focus on scalability, simplicity, agility and cost-efficiency.

What will you do:

• Build and maintain robust data pipelines (batch, micro-batch, real-time)
• Work with both structured and unstructured data from diverse sources
• Transform raw data into trusted, value-adding assets for business use
• Implement data quality frameworks (validation, profiling, cleansing)
• Manage storage, metadata and master data for the entire platform
• Support modern orchestration processes and pipeline automation
• Ensure systems are scalable, cost-effective, and easy to maintain