Python / C++ Developer | Signal Generation | Experienced Hire

As a Python / C++ Developer at Susquehanna, you'll be responsible for designing, developing, and scaling our signal generation platform. This means collaborating closely with our Quantitative Researchers and Strategy Developers, analyzing user needs, and determining the feasibility of software designs. Your work will directly support performance optimization in our high-frequency trading environment, ensuring that our systems operate efficiently.

Candidates for the Python / C++ Developer role at Susquehanna should have at least 6+ years of experience in C++ programming, particularly involving strict performance requirements. We also value candidates with deep knowledge of Linux, performance benchmarking, and a strong understanding of data structures and algorithms. Experience with Python for data analysis is also preferable, as it will play a crucial role in your responsibilities.

Key skills for success as a Python / C++ Developer at Susquehanna include strong systems fundamentals, deep Linux knowledge, and the ability to handle large scale computing challenges. Thick understanding of virtual memory, networking, and experience with building data transformation pipelines will also be essential to ensure our platforms perform optimally.

The work environment for a Python / C++ Developer at Susquehanna is collaborative and dynamic. You will partner with talented colleagues, including Quantitative Researchers and Strategy Developers, enabling a culture of innovation and problem-solving. It's a place where your contributions can make meaningful impacts while you grow and develop your skills among industry leaders.

Susquehanna encourages continuous learning and growth for their Python / C++ Developers. As you work alongside experienced professionals in proprietary trading, you'll gain insights into innovative technology and advanced algorithms. The company’s culture values curiosity and the desire to innovate, ensuring that you can develop your expertise and advance your career in the ever-evolving financial technology landscape.

To ensure high performance in my C++ applications, I focus on writing efficient algorithms, minimizing memory overhead, and profiling my code frequently to identify bottlenecks. Using tools like Valgrind or gprof allows me to analyze and optimize performance issues, ensuring that the application meets the exacting standards required in high-frequency trading.

In my previous roles, I have extensively utilized various data structures such as trees, graphs, and hash tables, depending on the use case. I am familiar with algorithmic complexities and strive to choose the best data structure to optimize performance and efficiency, important for real-time trading scenarios at Susquehanna.

Common performance trade-offs I've encountered include balancing memory usage with speed. For instance, optimizing for a faster lookup may involve using more memory if I choose a complex data structure. Understanding these trade-offs helps me make better design choices, ensuring the application meets both space and performance criteria.

In my previous position, I worked closely with quantitative analysts to gather detailed requirements for a new trading algorithm. I employed techniques like user interviews and prototype testing, which allowed me to develop a solution that met their performance expectations and operational requirements effectively.

I am highly familiar with Linux and use the command line extensively for software development tasks. I have leveraged shell scripting for automation and system monitoring, which is crucial in optimizing performance and integrating my applications into the trading infrastructure at Susquehanna.

I have previously developed data processing pipelines to handle large datasets efficiently. By utilizing Python for data manipulation combined with C++ for performance-critical sections, I ensured that the system could process millions of records in a short amount of time, perfectly aligning with the needs of a high-frequency trading environment.

For debugging performance issues, I typically employ a systematic approach starting with profiling tools to identify bottlenecks. Once identified, I utilize logging mechanisms to trace and isolate the problems. I also perform regression tests to ensure that fixes do not lead to unintended performance degradation.

My experience with collaboration in software development is extensive, as I often work alongside cross-functional teams. I value open communication and frequent feedback, which helps refine our designs and leads to more effective solutions. At Susquehanna, this collaboration would translate to better problem definitions and enhanced technical solutions.

My motivation in high-frequency trading technology comes from the unique challenges it presents. The need for optimized algorithms and systems that operate under strict time constraints drives my passion for efficiency and innovation, aligning perfectly with my skills and expertise at Susquehanna.

I am committed to staying updated with the latest trends in software development through regular reading of industry blogs, participating in online forums, and attending conferences. I often engage in continuous learning through online courses and collaborating with peers to share knowledge and insights, which keeps my skills sharp for a dynamic environment like Susquehanna's.