Azure Data Engineer

Industry: Financial market infrastructure

Contract: 12 months (extendable)

Start Date: ASAP

Job Description

Our client is a leading international financial market infrastructure organisation operating large-scale, mission‑critical platforms across Europe. The business is investing heavily in modern cloud and data technologies and is building a polyvalent data engineering team where engineers own data products end‑to‑end.

The environment is collaborative and engineering‑led, with a strong focus on quality, scalability, and long‑term platform evolution.

Role Overview:

We are looking for an Azure Data Engineer to design, build, and maintain modern data products across cloud and on‑prem platforms. You will work across the full data lifecycle - from ingestion and transformation through to analytics and visualisation - within a modern lakehouse architecture.

This is a hands‑on role suited to engineers who enjoy moving across the stack: cloud infrastructure, Spark pipelines, SQL modelling, and analytics layers.

Key Responsibilities:

• Design and orchestrate data pipelines on Azure (ADF, Functions, Event Hub, Lakehouse).
• Build scalable data transformations using Databricks / Spark.
• Integrate cloud and on‑prem data platforms.
• Model and optimise datasets using SQL, Python, and Scala/Java.
• Implement CI/CD, data quality checks, governance, and performance optimization.
• Contribute to lakehouse‑based data architectures.

Analytics & Consumption:

• Build curated semantic models and datasets for downstream consumption.
• Create Power BI datasets and dashboards where required (optional but valued).

Required Skills & Experience:

• Strong experience with Azure data engineering.
• (Data Lake, ADF, Key Vault, Functions, Event Hub).
• Hands‑on Databricks + Spark, including structured streaming and performance tuning.
• Advanced SQL for data modelling and optimization.
• Java or Scala (nice to have but highly valued).
• Experience working with lakehouse architectures.
• Understanding of CI/CD, data governance, and reliability in enterprise environments.