About the job
Trexquant is on the lookout for an exceptional Senior Data Architect to spearhead the design and implementation of a cutting-edge architecture for our research and simulation data ecosystem. This pivotal role will unify Trexquant’s vast array of datasets—sourced from numerous vendors—into a streamlined, efficient, and scalable data platform that caters to simulation, research, and alpha generation across diverse asset classes.
The ideal candidate will construct the comprehensive data infrastructure that empowers researchers and simulators to effortlessly discover, query, and integrate datasets spanning equities, futures, FX, ETFs, corporate bonds, and options. This role entails designing effective data models, storage solutions, and user-friendly interfaces that facilitate the transformation of raw vendor data into structured, analysis-ready formats, thereby enhancing systematic research and robust backtesting.
Responsibilities
- Design and execute a unified data platform that consolidates hundreds of vendor datasets, ensuring consistent, accessible, high-quality data for simulators and researchers.
- Create efficient storage and retrieval systems to enable large-scale historical backtesting and high-frequency research workflows.
- Develop user-friendly interfaces and APIs that facilitate easy variable discovery, metadata exploration, and data assembly into standardized stocks × values matrices for rapid hypothesis testing.
- Work collaboratively with quantitative researchers and simulation teams to grasp their workflows, ensuring the data platform meets analytical and performance needs in real-world scenarios.
- Establish best practices for data modeling, normalization, versioning, and quality control across various asset classes and data vendors.
- Collaborate with infrastructure and DevOps teams to enhance data pipelines, caching mechanisms, and distributed storage for improved scalability and reliability.
- Prototype and deploy internal data applications aimed at bolstering research productivity and data transparency.
- Guide and mentor data engineers to uphold robust, maintainable, and well-documented data systems.

