About the Role
Build and optimize robust data pipelines that power trading algorithms, ensuring data accuracy, low latency, and system reliability across global markets.
Responsibilities
- Develop and manage high-throughput data pipelines for market data feeds
- Design scalable storage solutions for time-series financial data
- Ensure data integrity and consistency across distributed systems
- Optimize data retrieval performance for algorithmic trading models
- Collaborate with researchers to integrate new data sources
- Monitor data systems for anomalies and performance degradation
- Implement automated data validation and error detection workflows
- Support real-time streaming data processing infrastructure
- Maintain documentation for data architectures and workflows
- Troubleshoot data flow issues across multiple environments
- Improve data pipeline reliability and fault tolerance
- Work with low-latency system requirements for trading signals
- Integrate third-party financial data providers into internal systems
- Ensure compliance with data usage policies and licensing
- Contribute to disaster recovery and backup strategies for data assets
- Evaluate new database technologies for performance gains
- Support backtesting infrastructure with historical data sets
- Automate deployment and scaling of data services
- Collaborate on schema design for heterogeneous data sources
- Improve monitoring and alerting for data pipeline health
- Assist in capacity planning for growing data volumes
- Implement secure access controls for sensitive data
- Optimize data compression and storage costs
- Work closely with infrastructure teams on network performance
- Contribute to code reviews and engineering best practices
Compensation
Competitive salary with performance-based bonuses
Work Arrangement
Hybrid work model with office and remote flexibility
Team
Collaborative team of engineers and quantitative researchers
Technology Stack
- Primary languages include Python and SQL
- Uses Apache Kafka for real-time data streaming
- Relies on PostgreSQL and specialized time-series databases
- Infrastructure hosted on AWS with Kubernetes orchestration
- Monitoring via Prometheus and Grafana
Growth Opportunities
- Opportunities to lead data architecture initiatives
- Access to training in emerging data technologies
- Mentorship from senior engineering and research staff
- Chance to contribute to core trading system improvements
- Regular performance reviews with career path planning
Available for qualified candidates
