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Data Developer – Fixed Income Electronic Trading

About the Role

We are seeking a Data Developer to join a Fixed Income technology team, supporting electronic trading and market-making operations. This hybrid role combines software engineering with data science, building analytical tools, machine learning models, and data products that directly inform trading decisions and improve execution performance.

You will work on the trading floor alongside traders, quants, and technologists to develop insights from market and transaction data, automate analytical workflows, and deploy models into production environments. This is a hands-on role for someone who enjoys both building robust systems and extracting value from complex datasets.

Responsibilities

* You will design and build data pipelines ingesting real-time and historical data from electronic trading platforms including MarketAxess, Tradeweb, and Bloomberg, as well as internal order management and execution systems.

* The role involves developing machine learning models for applications such as trade flow prediction, execution quality analysis, client behaviour segmentation, and market pattern recognition.

* You will create interactive dashboards and analytical tools for trading desk consumption, translating complex data into actionable insights.

* Working with quantitative researchers, you will productionise models and ensure they integrate seamlessly with trading infrastructure.

* The position requires automating analytical workflows, maintaining data quality and governance, and contributing to the team's broader data platform strategy.

* You will collaborate across functions to identify opportunities where data science can add value, communicating findings to both technical and non-technical stakeholders.

Requirements

* Programming Skills: Strong programming ability in Python is essential, including proficiency with data science libraries such as pandas, NumPy, scikit-learn, and visualisation tools.

* Experience building production-quality code and deploying models is important.

* Familiarity with SQL and relational databases required.

* Experience with Java, Kotlin, or Rust is beneficial

* Data Science: Solid grounding in statistical analysis, machine learning, and predictive modelling.

* Experience working with large-scale, time-series, or event-driven datasets.

* Ability to design experiments, validate models, and interpret results critically.

* Data Engineering: Experience building ETL pipelines and working with streaming technologies such as Kafka is beneficial.

* Familiarity with cloud platforms (AWS, Azure, or GCP) and modern data stack tooling.

* Communication: Ability to explain technical findings to traders and business stakeholders.

* Comfortable working in a fast-paced, collaborative environment on the trading floor.

* Domain Exposure: Prior experience in financial services is helpful but not essential.

* Willingness to learn fixed income products, electronic trading workflows, and market data structures.

Desirable

* Experience with Bloomberg data feeds or financial market data.

* Familiarity with NLP techniques for unstructured data analysis.

* Exposure to time-series forecasting or real-time analytics platforms.

* Understanding of regulatory reporting requirement