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Trexquant

Quantitative trading via ML models

Senior Data Architect

Data EngineeringUpdated on Nov 13
Full-time

About the Job

Requirements

  • At least 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, such as columnar databases, time-series systems, object stores, or data lakes.
  • Strong proficiency in Python and familiarity with modern data stack technologies, including Parquet, Arrow, Spark, SQL/NoSQL, and 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.
  • A bachelor's, master's, or Ph.D. in Computer Science, Engineering, Mathematics, or a related quantitative field.
  • Strong collaboration, communication, and documentation skills.

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 large-scale historical backtesting and high-frequency research workflows.
  • Develop researcher interfaces and APIs that enable users to discover variables, explore metadata, and assemble data into standardized stocks × values matrices for hypothesis testing.
  • Collaborate with quantitative researchers and simulation teams to understand their workflows and ensure the data platform meets 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 to enhance research productivity and data transparency.
  • Mentor and guide data engineers to maintain robust, maintainable, and well-documented data systems.

Desired Qualifications

  • Familiarity with cloud-based architectures, such as AWS, GCP, or Azure, and modern data governance practices.

About the Company

Trexquant

Quantitative trading via ML models

Trexquant is a quantitative finance firm that builds multi-asset portfolios using machine learning. It develops investment and research tools that ingest a wide range of data variables to create complex trading models and strategies, which in turn generate trading signals. These signals guide decisions across global markets. The product works by applying advanced machine learning to large datasets to create predictive models and rule-based strategies, which drive portfolio construction and trading actions through the firm’s platform. Trexquant differentiates itself by emphasizing the breadth of data it uses and the depth of machine-learning driven models to craft multi-asset portfolios, aiming to produce signals that outperform global market conditions. The company’s goal is to generate investment performance that beats benchmarks by using data-driven signals and diversified, multi-asset strategies.

Company size
51-200
Company stage
Not listed
Total funding
Not listed
Headquarters
Stamford, CT
Founded
2012

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