Data Engineer – Financial Infrastructure & Analytics

About the Role

As a Quantitative Data Engineer , you will be the backbone of the data ecosystem powering our quantitative research, trading, and AI-driven strategies . You will design, build, and maintain the high-performance data infrastructure that enables low-latency, high-fidelity access to market, fundamental, and alternative data across multiple asset classes.

This role bridges quant engineering, data systems, and research enablement , ensuring that our researchers and traders have fast, reliable, and well-documented datasets for analysis and live trading. You’ll be part of a cross-functional team working at the intersection of finance, machine learning, and distributed systems .

Responsibilities

• Architect and maintain scalable ETL pipelines for ingesting and transforming terabytes of structured, semi-structured, and unstructured market and alternative data.
• Design time-series optimized data stores and streaming frameworks to support low-latency data access for both backtesting and live trading.
• Develop ingestion frameworks integrating vendor feeds (Bloomberg, Refinitiv, Polygon, Quandl, etc.), exchange data, and internal execution systems.
• Collaborate with quantitative researchers and ML teams to ensure data accuracy, feature availability, and schema evolution aligned with modeling needs.
• Implement data quality checks, validation pipelines, and version control mechanisms for all datasets.
• Monitor and optimize distributed compute environments (Spark, Flink, Ray, or Dask) for performance and cost efficiency.
• Automate workflows using orchestration tools (Airflow, Prefect, Dagster) for reliability and reproducibility.
• Establish best practices for metadata management, lineage tracking, and documentation.
• Contribute to internal libraries and SDKs for seamless data access by trading and research applications.

In Trading Firms, Data Engineers Typically :

Tech Stack

What You Will Gain

Qualifications