Credit Data Engineer
We are partnered with a leading trading firm seeking a Data Engineer to join their Global Data Engineering team in New York City, with a focus on supporting the Credit Trading team and advancing ETF and portfolio-based systematic strategies.
Responsibilities:
- Develop innovative analytics and frameworks to evaluate trade-offs between sources of returns, flow, and risk.
- Collaborate with Quantitative Researchers and the Global Market Data team to quantify market drivers and support risk-taking decisions.
- Model and normalize complex financial datasets to ensure they are clean, structured, and accessible for research and trading systems.
- Own the reliability and performance of datasets and associated data APIs.
- Partner with alpha researchers, pricing researchers, and engineers to deliver scalable, reliable data solutions that meet evolving needs.
- Implement robust validation, monitoring, and engineering practices to maintain data quality and consistency.
- 3+ years of experience as a Data Engineer, Data Scientist, or similar role in a high-performance environment, ideally in financial services.
- Strong proficiency in Python and libraries such as Pandas, Arrow, and Spark.
- Solid understanding of data modeling, normalization, and API development for large-scale analytical or trading systems.
- Experience with lakehouse architectures (e.g., Delta Lake, Databricks, AWS) for managing large-scale analytical datasets.
- Familiarity with data sources relevant to Fixed Income trading, including CDS, corporate/capital structure, and bond NAVs.
- Strong software engineering fundamentals, especially around performance, scalability, and reliability in distributed systems.
- Exposure to statistical models and quantitative finance methods.
- Exceptional attention to detail, organizational skills, and project management capabilities.
- Ability to thrive in a fast-paced, team-oriented environment.
- Legal authorization to work in the U.S. is required; sponsorship is not available for this role.
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