Position

In the Data Engineer role, you’ll expand your expertise by contributing to modern initiatives across a variety of industries and business domains. You’ll work with a broad toolset, with responsibilities including:

Responsibilities

• Creating, developing, and operating scalable, efficient, and dependable data pipelines
• Delivering end-to-end data platforms, including data architecture and ETL implementations
• Partnering with data scientists, analysts, and engineering teams to integrate data and improve end-to-end performance
• Applying best practices for data governance, quality, and security across the data estate
• Tuning and streamlining data workflows to maximize reliability and throughput
• Keeping current with new trends and advancements in data engineering
• Supporting and coaching junior data engineers through mentoring and knowledge sharing
• Opportunity to grow your skills in advanced AI technologies

Tech stack

You’ll use a range of tools depending on the project, however our core stack typically includes:

Databricks, PySpark, Azure cloud and services (Data Lake, SQL Database, Azure Databricks, Azure Data Factory), SQL, Python, MS Fabric

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Must-have skills

To be successful in this position, you should have hands-on commercial experience with:

• Azure cloud and services (e.g. Azure Data Factory, Data Lake, SQL Database, Azure Databricks)
• Databricks (commercial project experience)
• Python and PySpark for building and operating data solutions
• SQL for querying, transforming, and validating data
• Core data engineering practices (ETL, data modeling, data warehousing, data governance)
• English at B2 level (or higher)

Nice to have:

• Hands-on experience with MS Fabric
• Familiarity with containerization and orchestration (Docker/Kubernetes)
• Understanding of machine learning concepts and frameworks (e.g. MLflow, TensorFlow)
• Knowledge or experience with LLMs and orchestration frameworks

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