Data Engineer

Summary

The Data Engineer will design, build, and optimize the data pipelines and models that support the firm’s evolving research, analytics, and systematic portfolio construction environment. This role is central to enabling data-driven investment processes, including quantitative research, AI/ML capabilities, and front-office automation.

Candidates must have deep expertise with Snowflake, strong SQL skills, and experience integrating diverse datasets used across investment organizations. The role is highly collaborative and requires comfort working in an iterative, fast-moving environment where data needs evolve rapidly based on stakeholder input.

Responsibilities

• Design, build, and enhance ETL/ELT pipelines in Snowflake, ensuring high performance, reliability, and scalability.
• Integrate internal and external datasets, including pricing, research content, economic releases, market data, and security reference data.
• Support real-time or near-real-time data flows where needed (e.g., pricing, indicative quotes, market-sensitive inputs).
• Collaborate closely with Product Leads, Quant Developers, and UI/UX teams to ensure data structures meet the requirements of research workflows, analytical models, and user-facing applications.
• Partner with front-office stakeholders to rapidly iterate on evolving analytical and data needs.
• Implement data validation, monitoring, and quality frameworks to ensure accuracy and reliability across critical datasets.
• Translate prototype pipelines into production-ready workflows with appropriate documentation, standards, and controls.
• Contribute to data modeling standards, metadata frameworks, and data governance practices across the platform.

Requirements

• 10 years of data engineering experience within investment management, financial technology, or similar data-intensive environments.
• Expert-level SQL, including complex queries, schema design, and performance optimization.
• Deep hands-on experience with Snowflake, including advanced features such as tasks, streams, performance tuning, and secure data sharing.
• Strong Python capabilities for ETL/ELT development, data processing, and workflow automation.
• Experience integrating APIs and working with structured, semi-structured, and unstructured datasets.
• Familiarity with NLP or AI/ML-oriented datasets (e.g., textual research content, PDFs) is a plus.
• Experience with Domino or willingness to work within a Domino-based model environment.
• Working knowledge of investment data structures (holdings, benchmarks, pricing, exposures) is highly preferred.
• Ability to thrive in a rapid prototyping environment with evolving requirements and close partnership with front-office teams.