Data Engineer | Sports | Experienced

Overview

Susquehanna is one of the world’s leading options market makers, providing liquidity across global financial markets through cutting‑edge technology, quantitative research, and deep trading expertise. We are looking for an experienced Quantitative Data Engineer to join our Prediction Markets team.

We specialize in geopolitical and sports analytics across the globe. This is an opportunity to build a next‑gen sports trading system within an early‑stage initiative focused on influential product development. Technology is at the forefront of everything we do, requiring innovative expertise. Our technologists enjoy working on highly complex in‑house software that runs on the most modern platforms, solving a wide range of problems to help us grow.

Our people continue to grow and learn throughout their careers as market demands change the nature of our environment, thanks to our flat structure and collaborative environment.

You will play a crucial role in designing, coding, and maintaining our next‑gen analysis and trading platform. You will work closely with Quantitative Researchers and other developers to:

• Source and cleanse data, develop and maintain data pipelines as required by quant research and trading.
• Assist with researching new trading opportunities.
• Develop, maintain and deploy interactive and packaged reports that will directly influence trading strategies.
• Performance‑tune existing applications and processes, improve existing codebases and data flows to allow for efficient processing of large data sets.

We seek a candidate with strong technical skills, a collaborative mindset, and a passion for sports wagering and trading.

• Experience: Experience with numeric data storage methodologies, processing large and varied data sets, and high‑performance implementation of mathematical algorithms. Strong interpersonal and communication skills for interacting with traders, quantitative analysts, and other software developers.
• Skills: Strong proficiency in the Python data stack (Num Py, pandas, etc.) and a solid understanding of Agile development methodologies. Experience with Kafka, Kubernetes, and cloud computing is highly desired.
• Education: PhD/MSc/BS degree in Computer Science or a related STEM field, or equivalent practical experience at the senior level.
• Interests: A keen interest in sports wagering, trading, and technology. Previous experience working within trading, sports analytics, or sports wagering industries is highly preferred.
• Attributes: Self‑motivated, detail‑oriented, and able to adapt to changing priorities. Excellent interpersonal and communication skills.

• Innovative Environment: Work on highly complex in‑house software using the latest platforms.
• Growth Opportunities: Continuous learning and career development in a flat, collaborative structure.
• Mentorship: Work with industry veterans and technical experts who are passionate about mentoring and sharing their knowledge.
• Perks: Excellent salary, benefits, and opportunities to attend world sporting events.
• Team: Join a small but growing team of skilled and passionate researchers, quants, and developers.

Susquehanna is a global quantitative trading firm founded by a group of friends who share a passion for game theory and probabilistic thinking. We have incorporated this approach into our culture, where you will find relentless problem solvers within each of our core disciplines:
Trading, Technology, and Quantitative Research. From offices around the world, our employees collaborate to make optimal decisions and are driven by the desire to achieve winning results together.

We are experts in trading essentially all listed financial products and asset classes, with a focus on derivatives trading. Through market making and market taking, we handle millions of trading transactions around the world every day, providing liquidity and ensuring competitive prices for buyers and sellers. While our presence in the market is broad, our trading desks are highly specialized, allowing for a deep understanding of unique drivers of each asset class.