Software Engineer - Risk Technology

We are seeking a Software Engineer to help build the technology and analytical frameworks used to understand, measure, and optimize risk across the firm's trading businesses.

Working directly with Risk Managers and software engineers globally, you will develop systems that help the firm analyze exposures, scenarios, capital, liquidity, and other risk factors across all asset classes. You will have significant autonomy to improve existing platforms, build new tools, and pursue opportunities that make the risk organization more effective.

This role is ideal for someone who enjoys combining software engineering, data science, and financial markets to solve complex, high-impact problems.

Your Core Responsibilities: 

• Design and build analytical tools that help Risk Managers understand portfolio exposures, stress scenarios, capital usage, and emerging risks
• Build high-end dashboards and visualization tools for stakeholders globally.
• Work with large data sets, data pipelines that support storage, retrieval, and analysis across the risk organization.
• Partner directly with Risk Managers to identify opportunities and rapidly translate ideas into production systems while collaborating within the firm's global risk development organization.
• Collaborate with engineering, trading, quantitative research, operations, and data teams across the firm
• Take ownership of projects from concept through production deployment

Your Skills and Experience: 

• 3–5+ years of professional software engineering experience
• Strong development skills in Java and/or Python
• Experience building and supporting production systems
• Interest in financial markets, risk management, data science, and quantitative analysis
• Strong analytical and problem-solving skills
• Self-starter with a high degree of ownership and initiative
• Distributed systems and service-oriented architectures
• Kubernetes and containerized applications
• React and modern web development
• PostgreSQL, ClickHouse, and analytical databases
• Parquet, Hadoop, and large-scale data platforms
• Data pipelines, ETL frameworks, and APIs