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The Fixed Income Data Platform Engineering team researches, adapts, and deploys the latest open-source data platforms to meet Citi's unique needs. As part of the team, you'll build and maintain Citi's next-generation data platform targeted for quantitative scientists and traders to quickly iterate on and put into production analytical and trading tools.
Job Responsibility:
Lead Airflow platform development for Fixed Income tech
analyze system requirements, including identifying program interactions and appropriate interfaces between impacted components and sub systems
lead Sprint Planning, Tasking and Estimation of the assigned work for Airflow component
participate in component and service design for Python analytical services
mentor junior developers and conduct code reviews
work on bug resolution and application improvements, such as performance and maintainability
stay abreast with new trends in open source tooling and champion tools that could help improve efficiency of the Fixed Income quant and data science community
Requirements:
3 years of hands on experience in building an enterprise scale highly componentized application using Python incl. web frameworks like Django or Flask and data science tools - Pandas, Polars, Streamlit
3+ years experience with Docker and Kubernetes/Openshift
3+ years experience with Python LLM tools (OpenAI, Vertex, Langchain) is a plus
At least 1 year of Experience in Airflow or equivalent Python scheduling framework
Experience working in a Continuous Integration and Continuous Delivery environment and familiar with Jenkins, TeamCity, Code Quality Tools - SonarQube, etc.
Proficient in industry standard best practices such as Design Patters, Coding Standards, Coding modularity, Prototypes etc.
Unit testing frameworks - PyTest
Understanding of the SDLC lifecycle for Agile & Waterfall methodologies
Excellent written and oral communication skills
Nice to have:
Python LLM tools (OpenAI, Vertex, Langchain)
familiarity with Airflow or equivalent Python scheduling framework
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