Azure Data Engineer
Azure Data Engineer
Location: Paris, Hybrid (on-site 6 days per month)
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:
Azure Cloud & On‑Prem Platforms:
- 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.
Data Engineering:
- 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.
- Strong Python development skills.
- 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.
- Comfortable switching between Spark code → SQL modelling → analytics layer
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