Azure Data Engineer

Azure Data EngineerLocation: Paris, Hybrid (on-site 6 days per month)Industry: Financial market infrastructureContract: 12 months (extendable)Start Date: ASAPJob DescriptionOur 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