Python Developer
4 weeks ago
Qualifications and Experience Criteria:
- Bachelor's Degree in Computer Science, or related field of study
- 5+ years of prior professional programming experience minimum
- Advanced Python and strong SQL database knowledge required.
- Working knowledge of NoSQL database such as MongoDB and Cassandra would be useful but not essential.
- Experience with using Pandas/NumPy would be an advantage
- Intermediate knowledge of C# and basic knowledge of other object-oriented languages such as C++ or Java would be useful
- Experience with data manipulation, ETL, data analysis, and time series would be a major advantage
- Working knowledge of cloud-based technologies such as Azure and AWS
- Excellent communication skills and ability to interact with a wide range of stakeholders
- Experience with fixed income products and supporting a rates trading desk would be an advantage but not a must and candidates with a background outside of finance will also be considered
- Experience with market data, pricing, and risk concepts would be an advantage; Familiarity with market data solutions providers such as Bloomberg, Refinitiv, Reuters, Factset would be helpful.
- Experience with database development will be essential; identifying business and technical requirements and designing, developing and implementing database systems.
- Fast learner with the ability to adapt quickly, think laterally and work in a dynamic environment
Responsibilities:
- Participate in full life-cycle software development and write clean, tested, and well-documented code
- Contribute ideas for new features and identify areas for improvement proactively
- Be responsible for database development, architecture and infrastructure including data ingestion, processing and internal distribution, as well as analytical platforms
- Work directly with the key stakeholders to integrate & optimize research data processes and models
- Design and develop cloud-native solutions in C#/Python/SQL to promote consistency of use of the Firm-wide data platform across numerous analytical tools
- Build robust data pipelines from many sources of varied levels of data quality, supporting both research demands, ad-hoc client analytic requests, and the core production applications
- Drive a technology strategy with increased levels of automation to reduce risk and enhance data quality and associated controls
- Partner with the Data Science team to create next-generation risk models including machine-learning driven platforms and code sets
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