Volatility Data Engineer – Systematic Data Platform

The systematic data group is looking for a Data Engineer/Scientist to join our growing team. The team consists of content specialists, data scientists, analysts and engineers who are responsible for discovering, maintaining and analyzing sources of alpha for our portfolio managers. This is an opportunity for individuals who are passionate about quantitative investing. The role builds on individual’s knowledge and skills in four key areas of quantitative investing: data, statistics, technology and financial markets.

Given the growing success of our Systematic Volatility business, the ideal candidate will leverage options, reference, exchange and TIQ-level data sets to arm Portfolio Managers with the necessary information to make better, real-time investment decisions.

Responsibilities

• Build systematic option platform related to referential data, pricing data, data analysis and data research.
• Work closely with data scientists/analysts for the end-to-end life cycle of data.
• Support quantitative researchers/portfolio managers on data related used for signal generation, back testing and trading.

Qualifications

• Strong technical skills with experiences in production coding in Linux.
• At least 2 years of experience in finance, finance technology, or comparable industry – buyside experience preferred.
• Master’s degree or higher in fields such as quantitative finance, engineering, computer science or equivalent.
• Proficient in computer science fundamentals and object orientated programming using Python or C++ or Java or equivalent.
• Self-driven and eager to learn and able to pick up things quickly.
• Experience with databases/SQL and basic understanding of financial products.
• Hard-working, intellectually curious and team-oriented.
• Strong communication skills
• Experience with option security master or option data or option trading or option vol is strong plus
• Experience with technologies like KDB, Apache Iceberg, and Lake Formation will be a meaningful differentiator.