Senior Software Engineer, Post-Trade Financial Systems

Responsibilities

• Design and optimize robust, scalable data infrastructure and real-time stream processing systems to support historical and live pipelines using tools like Python, Airflow, Go, and Apache Beam.
• Develop and maintain observability and remediation tools to monitor and analyze trading performance and risk, ensuring reliability and transparency in operations.
• Lead efforts to integrate new financial assets and markets, clarifying requirements and ensuring seamless functionality within existing systems.
• Enhance the resilience, scalability, and performance of accounting and reporting systems to meet evolving business needs.
• Build advanced tooling to unify data from diverse vendors, standardizing symbol mappings to ensure consistency and accuracy across systems.
• Lead complex, company-wide projects by collaborating cross-functionally with research, legal, trading, finance operations, data, and infrastructure teams to deliver comprehensive end-to-end accounting and reporting systems.
• Build the infrastructure required for optimal extraction, transformation, and loading of data from various data sources.
• Guide and support the growth of other engineers on the team by mentoring them and sharing your expertise, best practices, and knowledge.

Requirements

• Bachelor’s degree in Computer Science or equivalent professional experience in a related technical field.
• 5+ years of software engineering experience designing and building high-performance, reliable systems.
• Proven expertise in operating and scaling large-scale, mission-critical production systems, with proficiency in programming languages such as Python.
• Strong communication and project management skills, particularly in navigating complex technical domains and cross-functional collaboration.
• Demonstrated ability to mentor engineers and provide leadership in driving technical direction and system architecture.

Preferred Qualifications

• Expertise in building and optimizing data pipelines (e.g. Change data capture, data modeling, data streaming, etc.).
• Experience with profiling and performance optimizations on distributed systems.
• Familiarity with modern Python data science tooling (pandas, polars, dask, duckdb, etc.).
• Experience with modern data engineering technologies.