Lab49/ION is looking to solve the problem of designing, enhancing, and maintaining the Market Risk Time Series infrastructure built on Snowflake and AWS, requiring strong technical skills combined with deep domain expertise in market risk.
Requirements
- Strong Python (pandas, numpy, data engineering best practices)
- Advanced SQL and Snowflake (warehouse management, streams/tasks, query optimization)
- 7+ years of hands-on experience in developing applications using Relational Databases and Big-data platforms
- Market risk concepts: VaR, SVaR, sensitivities, stress testing
- Handling end-of-day market data and historical time series across asset classes
- Ability to translate risk requirements into technical solutions and data contracts
Responsibilities
- Data Sourcing & Integration: Source historical market data from multiple internal and external providers. Integrate with quant libraries to identify data quality issues and validate risk inputs.
- Data Quality & Remediation: Integrate with Quant APIs to detect and remediate common data quality issues (gaps, stale data, outliers, misalignments). Implement algorithms for gap-filling, back-filling, and anomaly correction to ensure data is fit for VaR and SVaR calculations.
- Infrastructure Development: Build and enhance Snowflake-based time series infrastructure for scalability and performance. Develop Python ETL/ELT pipelines and optimized SQL models for historical time series storage and retrieval.
- Collaboration & Governance: Work closely with Market Data and Risk teams to define canonical market observables and maintain data lineage. Ensure reproducibility and auditability of risk inputs for regulatory compliance.
Other
- Bachelor’s degree, preferably in Computer Science, Engineering, Mathematics, or similar technical discipline
- Strong analytical and problem-solving skills, including the ability to troubleshoot and resolve complex data related issues
- Strong verbal and written communication skills
- Self-starter and entrepreneurial in approach
