Software Engineer

Own and evolve the research data platform supporting the quant team and affiliated research teams, with responsibility for the full lifecycle of data pipelines spanning market data and diverse proprietary datasets. Partner closely with quantitative researchers to deliver high-quality, scalable data infrastructure that directly enables research workflows and contributes to alpha generation.

Lead the design, build, and continuous improvement of robust data systems, ensuring seamless ingestion, normalization, and distribution of both historical and real-time data. Act as a key bridge between engineering and research, translating complex data requirements into performant, production-grade solutions.

Drive the modernization and scaling of the research platform, with a focus on reliability, latency reduction, and efficient access patterns. Champion automation across the data lifecycle to streamline workflows, reduce operational overhead, and accelerate research velocity.

Core Responsibilities

• Own the architecture and delivery of large-scale research data platforms, supporting both batch and real-time data processing.
• Build and maintain high-performance data pipelines for market and alternative datasets, ensuring accuracy, completeness, and timeliness.
• Partner with quantitative researchers to onboard new datasets and optimize data access for modeling and backtesting.
• Enhance platform scalability and resilience through improvements in system design, monitoring, and fault tolerance.
• Develop and refine API frameworks to provide intuitive, efficient access to research data across teams.
• Lead initiatives to automate data ingestion, validation, and transformation processes, reducing manual intervention and operational risk.
• Establish best practices for data governance, quality control, and lineage tracking across the platform.

Technical Expertise

• Strong programming skills in Python and modern C++ , with experience building production-grade data systems.
• Proven track record designing and optimizing high-throughput, low-latency platforms handling large volumes of structured and unstructured data.
• Deep experience working with time-series data , including both historical datasets and real-time streaming pipelines.
• Expertise in distributed systems, data storage solutions, and performance tuning for scalable infrastructure.
• Familiarity with building robust data APIs and frameworks that support flexible and efficient data access patterns.
• Demonstrated ability to leverage AI/ML tools and automation frameworks to enhance developer productivity and streamline data workflows.

Leadership & Impact

• Provide technical leadership across engineering teams, setting a high bar for system design, code quality, and operational excellence.
• Mentor and develop engineers, fostering a culture of ownership, innovation, and continuous improvement.
• Break down complex platform initiatives into clear, milestone-driven roadmaps with measurable outcomes.
• Drive end-to-end delivery of critical systems, ensuring solutions are scalable, maintainable, and production-ready.
• Collaborate effectively with cross-functional stakeholders including research, data, and infrastructure teams to align priorities and deliver impact.
• Influence strategic direction of the research data ecosystem through strong communication and thought leadership.