Data Engineering and Reporting Analyst

NO H1S OR 3RD PARTIES

• Design, build, and maintain high-performance data pipelines using advanced Python — including async patterns, generator-based streaming, distributed processing architectures, and caching strategies
• Develop and maintain GraphQL APIs for internal data services, enabling flexible, efficient querying across trading, risk, and analytics platforms
• Build and operationalize ML data pipelines: feature engineering, data validation, model serving infrastructure, and real-time inference support
• Integrate with external market data providers (e.g., Bloomberg) and internal systems including risk, settlement, and portfolio management platforms
• Optimize pipeline performance through profiling, memory management, and distributed execution strategies
• Partner with data scientists and ML engineers to productionize models and ensure data quality throughout the feature lifecycle
• Support production data systems during trading hours; participate in release management and deployment workflows
• Contribute to data platform architecture decisions, documentation, and engineering best practices

• 4–7 years of experience in data engineering, software engineering, or a closely related role
• Advanced Python proficiency: async/await, generators, multiprocessing, performance optimization, OOP design patterns
• Hands-on GraphQL experience: schema design, resolvers, query optimization, and API federation
• Machine learning pipeline experience: feature stores, data preprocessing, model serving, MLOps tooling (e.g., MLflow, Feast, Airflow/Prefect)
• Strong SQL and NoSQL skills; experience with Redis or other caching layers
• Experience with REST API design and integration (Flask or FastAPI preferred)
• Familiarity with distributed systems and scalable data architecture patterns
• Experience in financial services, trading systems, or data-intensive production environments a strong plus
• B.S. in Computer Science, Information Technology, Engineering, or equivalent practical experience

• Experience with LangChain, LLM APIs, or AI agent frameworks (e.g., MCP protocol)
• Familiarity with Bloomberg API, FactSet, or other market data integrations
• Exposure to fixed-income, derivatives, or collateral management data domains
• Experience with cloud data platforms (AWS, Google Cloud Platform, or Azure) and containerized deployments
• Track record supporting global production releases or serving as release manager