Role responsibilities: - Design and maintain scalable backend systems and APIs that support quantitative research and trading infrastructure. - Develop distributed compute applications, optimize performance, and implement monitoring and observability tooling to ensure system reliability. - Build high-performance data pipelines for processing large-scale financial datasets using time series data, SQL, and NoSQL databases. - Partner with engineers, researchers, and platform teams to translate complex requirements into technical solutions that integrate with the firm’s technology estate. - Participate in production support rotations and contribute to architecture decisions, code reviews, and engineering best practices across the team. - Leverage AI development tools to accelerate delivery and drive their adoption across the team.

Technology: You will work in a modern Linux-based infrastructure where Python is the primary development language, using the full scientific stack including pandas, numpy, scipy, and scikit-learn. A key component of the data infrastructure is Arcti

Role responsibilities: - Design and maintain scalable backend systems and APIs that support quantitative research and trading infrastructure. - Develop distributed compute applications, optimize performance, and implement monitoring and observability tooling to ensure system reliability. - Build high-performance data pipelines for processing large-scale financial datasets using time series data, SQL, and NoSQL databases. - Partner with engineers, researchers, and platform teams to translate complex requirements into technical solutions that integrate with the firm’s technology estate. - Participate in production support rotations and contribute to architecture decisions, code reviews, and engineering best practices across the team. - Leverage AI development tools to accelerate delivery and drive their adoption across the team.

Technology: You will work in a modern Linux-based infrastructure where Python is the primary development language, using the full scientific stack including pandas, numpy, scipy, and scikit-learn. A key component of the data infrastructure is Arcti