Data Operations Engineer

One of our clients in the Trading & Market Making space is looking for an engineer to serve as a frontline point of contact for traders, researchers and internal users of data platforms within the London based trading team.

This role sits in the nexus between Data Engineering and Reliability & Operations Engineering, and is an excellent opportunity to work in a dynamic, impactful function within one of the most cutting edge trading environments in the city!

What You’ll Do

• Serve as frontline POC for traders, internal users and research teams for everything relating to data reliability.
• Investigate and resolve data quality concerns and freshness anomalies.
• Monitor ingestion pipelines, processes and real time feeds.
• Triaging and addressing alerts promptly to minimize impact on trading.
• Manage relationships with external vendors and resolve upstream issues, specification changes and ensure accurate delivery of datasets.
• Document and relay clear updates to users during production events.

Technical responsibilities

• Ingest, configure and operationalise new datasets.
• Develop and maintain ETL/ELT data pipelines to feed real time trading and research systems.
• Implement data quality checks, anomaly detection and monitoring frameworks.
• Build and maintain high performance API’s to expose market and reference data to trading, research and analytics platforms.

Who you are

• 3+ years in Data Engineering, SRE, SWE or Data Ops roles in high performance, time sensitive environments.
• Strong Python proficiency, including exposure to libraries such as Pandas, Arrow, and Spark.
• Strong grasp of data modelling, normalization and API development for large scale analytical or trading systems.
• Experience with lakehouse architectures (Databricks ideally, or Delta Lake).
• Exposure to real time and historical market data within Fixed Income, ETFs or Equities would be excellent.
• Operational instincts - extreme agency and ownership over issues, tackling issues with initiative.

• 3+ years in Data Engineering, SRE, SWE or Data Ops roles in high performance, time sensitive environments.
• Strong Python proficiency, including exposure to libraries such as Pandas, Arrow, and Spark.
• Strong grasp of data modelling, normalization and API development for large scale analytical or trading systems.
• Experience with lakehouse architectures (Databricks ideally, or Delta Lake).
• Exposure to real time and historical market data within Fixed Income, ETFs or Equities would be excellent.
• Operational instincts - extreme agency and ownership over issues, tackling issues with initiative.