3 months ago
London, United KingdomStaff+
Responsibilities
- Source historical market data from internal and external providers and integrate it with quant libraries and APIs.
- Detect and remediate data quality issues such as gaps, stale data, outliers, and misalignments.
- Implement algorithms for gap-filling, back-filling, and anomaly correction for VaR and SVaR calculations.
- Build and enhance scalable Snowflake-based historical time series infrastructure.
- Develop Python ETL/ELT pipelines and optimized SQL models for time series storage and retrieval.
- Collaborate with Market Data and Risk teams to define canonical market observables and maintain data lineage.
- Ensure risk inputs are reproducible, auditable, and suitable for regulatory compliance.
- Translate market risk requirements into technical solutions and data contracts.
Requirements
- At least 7 years of hands-on experience developing applications with relational databases and big-data platforms.
- Strong Python skills, including pandas, NumPy, and data engineering best practices.
- Advanced SQL and Snowflake experience, including warehouse management, streams, tasks, and query optimization.
- Knowledge of market risk concepts including VaR, SVaR, sensitivities, and stress testing.
- Experience handling end-of-day market data and historical time series across asset classes.
- Ability to translate risk requirements into technical solutions and data contracts.
- Bachelor’s degree, preferably in Computer Science, Engineering, Mathematics, or a similar technical discipline.
- Strong analytical, problem-solving, communication, troubleshooting, prioritization, and follow-through skills.
