Senior Data Architect (USA)

Trexquant is seeking a highly skilled Senior Data Architect to design and lead the next-generation architecture for our research and simulation data ecosystem. This role is central to unifying Trexquant’s extensive collection of datasets—sourced from hundreds of vendors—into an accessible, efficient, and scalable data platform that supports simulation, research, and alpha generation across multiple asset classes.

The successful candidate will architect the end-to-end data infrastructure that enables researchers and simulators to seamlessly discover, query, and combine datasets across equities, futures, FX, ETFs, corporate bonds, and options. This person will design data models, storage systems, and researcher-facing interfaces that make it easy to transform raw vendor data into structured, analysis-ready forms—empowering systematic research and robust backtesting.

Responsibilities

• Architect and implement a unified data platform that integrates hundreds of vendor datasets, providing consistent, accessible, and high-quality data to simulators and researchers.
• Design efficient storage and retrieval systems to support both large-scale historical backtesting and high-frequency research workflows.
• Develop intuitive researcher interfaces and APIs that allow users to easily discover variables, explore metadata, and assemble data into standardized stocks × values matrices for rapid hypothesis testing.
• Collaborate closely with quantitative researchers and simulation teams to understand their workflows, ensuring the data platform meets real-world analytical and performance needs.
• Establish best practices for data modeling, normalization, versioning, and quality control across asset classes and data vendors.
• Work with infrastructure and DevOps teams to optimize data pipelines, caching, and distributed storage for scalability and reliability.
• Prototype and deploy internal data applications that enhance research productivity and data transparency.
• Mentor and guide data engineers to maintain robust, maintainable, and well-documented data systems.

Requirements

• 7+ years of experience in data architecture, quantitative research infrastructure, or large-scale data engineering in a financial or research-driven environment.
• Proven experience designing and implementing scalable data storage solutions (e.g., columnar databases, time-series systems, object stores, or data lakes).
• Strong proficiency in Python and familiarity with modern data stack technologies (e.g., Parquet, Arrow, Spark, SQL/NoSQL, distributed file systems).
• Deep understanding of time-series and financial data modeling, including handling multiple vendors, instruments, and frequencies.
• Experience building data interfaces, APIs, or tools that serve researchers, data scientists, or quantitative analysts.
• Ability to translate research needs into efficient data schemas and access patterns.
• Bachelor’s, Master’s, or Ph.D. in Computer Science, Engineering, Mathematics, or a related quantitative field.
• Strong collaboration, communication, and documentation skills.
• Familiarity with cloud-based architectures (e.g., AWS, GCP, Azure) and modern data governance practices is a plus.