About the Role
We are seeking a highly skilled and entrepreneurial Lead Software Engineer to architect, build, and scale the core trading infrastructure for a newly formed quantitative trading pod. In this hands-on leadership role at the intersection of technology, research, and trading, you will collaborate directly with quantitative researchers and the Senior Portfolio Manager to turn ideas into production-ready strategies. This is a unique opportunity to own the technology stack from day one, influence strategic direction, and help shape a high-performance team in a collaborative and intellectually rigorous environment.
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Location
Dubai
Principal Responsibilities
- Technical Leadership & Architecture
- Design and maintain the trading system architecture, including components for data ingestion, signal generation, backtesting, execution, and risk management
- Make decisions on technology stack, performance optimization, and scalability
- Ensure the system supports reasonable latency, high-throughput, and fault-tolerant trading
- Team Leadership & Collaboration
- Help recruit and lead a small team of developers (sometimes quants as well) within the pod
- Work closely with the quant researchers and traders to understand strategy requirements and translate them into code
- Prioritize tasks and mentor junior developers or quant devs
- Strategy Implementation Support
- Build and maintain research infrastructure (e.g., backtesting frameworks, simulation environments, feature stores)
- Translate researchers' prototypes (e.g., in Python) into production-grade code, often in C++, or C# for latency-sensitive components
- Execution & Infrastructure
- Optimize and support order execution systems, integrating with various exchanges or broker APIs
- Implement real-time risk checks, monitoring, logging, and alerting tools
- Data Engineering & Management
- Oversee the pipeline for ingesting, cleaning, and storing data (market, alternative, internal)
- Ensure data integrity and low-latency access for trading and research
- DevOps & Reliability
- Often take responsibility for deployment pipelines, version control, and production support
- Ensure high system availability and rapid recovery in case of failures
- Security & Compliance
- Make sure that the pod's infrastructure adheres to firm-wide compliance and security standards
- Ensure rigorous version control, code quality, and documentation standards
- Conduct thorough testing and debugging of software components, resolving any issues or discrepancies.
Required Technical Skills
- 1st class Bachelor's or Master's degree in Computer Science, Mathematics, Physics, Engineering, or a related quantitative field
- Fluency in C++ or C# for performance-critical systems
- Proficiency in Python, especially for scripting, research integration, and data tools
- Solid understanding of algorithms, data structures, and multithreaded/concurrent programming
- Strong knowledge of SQL and modern database design (e.g., column stores, time-series DBs)
- Familiarity with software engineering best practices: version control (Git), unit testing, CI/CD, logging, monitoring, etc.
- Strong troubleshooting skills across distributed systems
Required Experience
- 3+ years of hands-on experience designing, building, and maintaining high-performance trading systems, ideally in a systematic equities or quant trading environment
- Proven experience in:
- Handling large-scale market data (e.g., normalization, feed handling, replay systems)
- Order routing and exchange connectivity, including FIX protocols and direct market access (DMA)
- Building event-driven architectures and real-time systems with tight SLAs
- Identifying and resolving performance bottlenecks, data inconsistencies, or system instability in production environments
Highly Valued Relevant Attributes
- Excellent communication skills - able to interface directly with quant researchers and traders, translate requirements, and explain technical decisions
- Demonstrated initiative and ownership: able to drive projects independently, while collaborating effectively in a team setting
- Comfortable in fast-paced, iterative environments where priorities can shift quickly based on market conditions or research insights
