Software Engineer - Data (Java / Kotlin / Python)
Summary
A 6-month onsite contract in London at an elite quantitative research and trading firm: own and modernise large-scale financial market data pipelines, remap datasets, and upgrade legacy JVM systems using Kotlin/Spring Boot, SQL Server, Spark, and Python tooling. Financial services data pipeline experience is a hard prerequisite.
Software Engineer - Data (Java / Kotlin / Python)
Do not apply if you do not have experience within Financial services, working with Java data pipelines as you will not be considered.
6-month initial contract | Onsite (potential for hybrid)
An elite quantitative research and trading firm is looking for a Data focused Software Engineer to join a Reference Data team supporting critical research and trading use cases.
This team owns complex financial market datasets that describe how markets are structured and behave, and plays a key role in ensuring high-quality, reliable data flows across the organisation.
What you’ll be doing
- Owning and enhancing data pipelines that process large-scale financial market data
- Modernising existing data flows, upgrading legacy JVM-based pipelines into newer, cleaner architectures
- Remapping incoming datasets and rewriting ingestion and transformation pipelines
- Working across SQL Server, JVM applications, and Spark-based workloads
- Contributing improvements to data quality, performance, and maintainability
Tech environment
- Kotlin & Spring Boot for core data processing services
- Python for tooling and automation
- Legacy systems in C# and Scala / Spark
- Exposure to data lakes and large-scale data platforms is a strong plus
What they’re looking for
- Strong JVM background (Kotlin, Java, or Scala)
- Experience building or modernising data pipelines
- Solid understanding of SQL and data processing systems
- Comfortable working in complex, performance-sensitive environments
- Financial markets or trading data experience is beneficial but not essential
6-month initial contract with potential to extend. Onsite by default, with some flexibility for hybrid working.