Data Quality Engineer / Data Engineer Senior

Context & Challenges

Reference market data (futures, yields, FX, indices, bonds) present numerous undetected, untracked, and often manually corrected anomalies. This situation leads to significant time loss and impacts the confidence of Research & Prediction teams.

Mission Objective

Structure, validate, and industrialize quality controls on market data, using both technical and analytical approaches.

3 Main Axes

1. Audit
   • Complete mapping of existing controls
   • Identification of gaps by asset class
   • Construction of a prioritized backlog
2. Implementation
   • Development of a modular quality checks library in Python
   • Remediation of historical data
   • Management of escalations with data providers (Bloomberg)
3. Industrialization & AI
   • Implementation of automated monitoring (KPI dashboards, alerting)
   • Industrialization of production controls
   • Proof of Concept (POC) based on LLMs for:
     • Semi-automatic generation of controls
     • Root cause analysis of anomalies

Candidate Profile:

Data Quality Engineer / Senior Data Engineer (6 to 10 years of experience)

• Strong expertise in financial market data (Bloomberg indispensable)
• Excellent command of Python (development of robust and documented libraries)
• Experience in production deployment (monitoring, dashboards, alerting)
• Interest or concrete experience with LLMs applied to data
• Ability to work in complete autonomy in a demanding environment

Mission oriented towards consulting with a high level of requirement, without client-side supervision. Senior profile expected, capable of evolving in a quantitative hedge fund environment.