Software Developer – Risk Data Pipelines

Job ID: 6919Location: LondonSalary: 180,000-220,000 per yearPosted: 2026-07-08Software Developer – Risk Data PipelinesLondonCompetitive Salary + BonusNcounter is supporting a global quantitative investment manager whose risk platforms rely on high-quality, real-time data.

Read all the information about this opportunity carefully, then use the application button below to send your CV and application. This role sits within the engineering team responsible for the ingestion, transformation, storage, and delivery of market, position, and reference data into critical risk systems.

The focus is simple: ensure risk data is fast, accurate, complete, and verifiably fresh. In a trading environment, risk calculated on stale or incomplete data is worse than no risk data at all. This is a hands-on software engineering position for someone who enjoys building production-grade data systems and cares as much about data reliability as they do about clean code.

Key ResponsibilitiesDevelop and maintain production data pipelines supporting risk analytics platforms. Build reliable, recoverable, and observable data workflows. Improve the quality, freshness, and completeness of critical risk data.

Engineer performant Python applications for data processing and transformation. Optimise large-scale analytical data stores and query performance. Contribute to monitoring, alerting, and operational reliability across data services.

Experience RequiredExperience building and operating production data pipelines rather than one-off scripts or analysis tooling. Strong Python development skills, with experience building performant, maintainable applications.

Experience with workflow orchestration tools such as Airflow, Dagster, or Prefect. Understanding of retries, dependency management, idempotency, backfills, and operational recovery. Experience with analytical or columnar databases such as ClickHouse or similar technologies.

Knowledge of partitioning, materialised views, and query optimisation techniques. Experience with numerical and data processing libraries including NumPy, pandas, Polars, or Arrow. Understanding of performance optimisation, memory usage, multiprocessing, or asynchronous Python.

This opportunity would suit an engineer who enjoys solving complex data engineering problems and building the reliable data foundations that underpin modern risk systems.

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