Senior Market Risk Developer - Historical Time Series

Senior Market Risk Developer – Historical Time Series

Market Risk Technology | Time Series Infrastructure | Snowflake & AWS

LevelUp is recruiting on behalf of Jefferies , a leading global full-service investment bank and capital markets firm, for an experienced Senior Market Risk Developer to join their Risk Technology organisation.

This role sits within the team responsible for building and supporting a global risk platform that enables the firm’s Risk Management function to measure, analyse, report, and control market and credit risk across the business. The successful candidate will play a key role in the design, enhancement, and governance of historical market time series infrastructure , supporting critical risk analytics including VaR and SVaR .

This is a techno-functional position , requiring strong hands-on engineering skills combined with deep domain knowledge of market risk and risk data.

Key Responsibilities

Data Sourcing & Integration

• Source and integrate historical market data from multiple internal and external providers.
• Partner with Quant and Risk teams to validate risk inputs and identify data quality issues.
• Integrate with quantitative libraries and APIs to support accurate risk analytics.

Data Quality & Remediation

• Detect and remediate common market data issues including gaps, stale data, outliers, and misalignments.
• Implement algorithms for gap-filling, back-filling, and anomaly correction to ensure data is fit for VaR and SVaR calculations.
• Support ongoing improvements to data quality controls and monitoring.

Infrastructure & Platform Development

• Design, build, and enhance Snowflake-based historical time series infrastructure for scalability, performance, and reliability.
• Develop and maintain Python-based ETL/ELT pipelines and optimised SQL data models.
• Contribute to cloud-based architecture on AWS , supporting platform modernisation initiatives.

Collaboration & Governance

• Work closely with Market Data, Risk, and Quantitative Analytics teams to define canonical market observables.
• Maintain strong data lineage, reproducibility, and auditability of risk inputs to meet regulatory and governance requirements.
• Translate complex risk requirements into robust technical solutions and data contracts.

Experience, Skills and Qualifications

Required Experience

• 7+ years of hands-on experience developing applications using relational databases and big data platforms within financial services.
• Proven experience working with historical market data and time series in a market risk context.

Technical Skills

• Strong Python skills (pandas, numpy, data engineering best practices).
• Advanced SQL and strong experience with Snowflake (warehouse management, streams/tasks, query optimisation).
• Experience building scalable, production-grade data platforms.

Domain Knowledge

• Solid understanding of market risk concepts , including VaR, SVaR, sensitivities, and stress testing.
• Experience handling end-of-day market data across multiple asset classes.

Techno-Functional Capability

• Ability to translate risk and business requirements into technical solutions.
• Comfortable working at the intersection of Risk, Quant, and Technology teams.

Education

• Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related technical discipline.

Personal Attributes

• Strong analytical and problem-solving skills with the ability to troubleshoot complex data issues.
• Clear and confident verbal and written communication skills.
• Self-starter with an entrepreneurial mindset and strong ownership mentality.
• Proactive approach to escalation, follow-up, and delivery.
• Strong time management and organisational skills.

Why Join?

This is an excellent opportunity to work at the core of a global investment bank’s market risk infrastructure , supporting mission-critical analytics and regulatory processes. You will join a collaborative, technically strong environment with exposure to senior risk stakeholders, modern cloud-based platforms, and complex data challenges.

The role offers a hybrid working model , competitive compensation, and long-term career development within a globally recognised institution.