Data Engineer - Spark Python Scala

La Fosse Associates
London, GB
Hybrid

Job Description

Join a leading global financial services company and shape the future of data architecture in their Capital Markets division.

Salary: £550 - 625 per day

La Fosse is currently recruiting a contract Data Engineer (12 months) on behalf of a global financial services company.

They require an experienced engineer with extensive experience in building and maintaining Data Lakehouses in Azure. The ideal candidate will have a strong background in both Scala and Python programming, with specific experience building high performance and scalable pipelines in Databricks with Spark.

The new data platform will serve a range of end users in the Capital Markets division, so experience across areas such as derivatives, risk, PnL, trade lifecycle, market data are highly desired.

Key Responsibilities:

  • Engineer robust, scalable and efficient data pipelines using Spark/Databricks
  • Demonstrate best practices in the design & development of data pipelines
  • Data modelling on medallion architecture, ensuring schema is intuitive and serves business users analytical and reporting objectives

What you'll need:

  • Expert in building data pipelines in Databricks with Spark (preferably on Azure)
  • Financial Services sector knowledge (ideally covering trading, derivatives, risk and trade-lifecycle)
  • Expert level programming in Python (plus Scala is highly desired)
  • Expert data modelling experience on medallion architecture

Hybrid model: 1 day per week in City of London

Duration: 12 months

IR35 – Outside

Start date: April 2026

Take the next step in your data engineering career with this exciting opportunity to work on cutting-edge financial technology.

Reference: 119069

Skills & Requirements

Technical Skills

ScalaPythonDatabricksSparkAzureData LakehousesData ModellingMedallion Architecturefinancial servicescapital marketsderivativesriskPnLtrade lifecyclemarket data

Employment Type

CONTRACT

Level

mid

Posted

4/2/2026

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