Our client, a quantitative asset management firm, derives its unique edge in asset management from its blend of high-frequency trading and science-based investment approaches. They are looking for a data engineer to join their quantitative development team who will be a close partner to Equities Statistical Arbitrage trading team. This role can be based in New York, NY or Austin, TX. Data drives systematic trading and is critical to all aspects of the firm's business. This is a hands-on position with significant growth potential. The firm is looking for outstanding technical skills, strong attention to detail, and experience with both systems development and data modeling.
Responsibilities
- Communicate with quantitative researchers and other end-users to understand their requirements and potential future requests
- Investigate vendor data thoroughly to become a subject matter expert on its characteristics and irregularities
- Develop ETL processing components using cutting edge technologies, and write robust tests for on-going quality control
- Support developed transformations and ETL frameworks in production trading as well as backtest research
- Build flexible data API components in iterations with research peers to ensure their needs are met
- Optimize data IO and load balancing for distributed, grid computation
- Analyze a variety of large data sets to develop and implement alpha signals
Requirements
- Detail oriented
- Strong programming skills, preference for Python, Java is a plus
- Experience with the scientific Python stack, Numpy, Scipy, Pandas, Matplotlib
- Ability to find practical solutions and successfully make trade-offs between long-term goals and short-term deliverables
- Ability to troubleshoot difficult problems, both numerically and technically
- Proficient with SQL, experience with Postgres is a plus
- Experience with Hadoop, Spark, and Docker is a plus
Preferred Education and Experience
- Computer Science/Math or similar degree
- 2-5 years of professional experience, ideally exposure to work with complex data sets and all stages of cleaning, preparing data to be used in specific format
