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Trexquant Investment LP
Trexquant Investment LP
51 – 200 Employees
H1B Visa SponsorFinanceFintech
Trexquant Investment LP is a quantitative investment management firm focused on statistical arbitrage and medium-frequency trading. Its research-driven approach combines thousands of trading signals, or Alphas, built from diverse market data and rigorously back-tested for resilience across changing market conditions. The firm applies these strategies across more than 5,500 cash equity positions in global markets, including the United States, Europe, Japan, Australia, and Canada.
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Data Engineer in Beijing (Hybrid)

Build scalable pipelines for Trexquant’s financial and alternative datasets in a hybrid Beijing role. Support quantitative research with reliable, normalized data infrastructure.

CNChinaHybridFull-timeJuniorMiddleEnglish RequiredData Engineer
ETLLinuxPythonSQL

Description

  • Create, operate, and enhance scalable ingestion pipelines for market, reference, tick, and alternative data supplied by external vendors.
  • Manage the normalization, validation, storage, and lifecycle of research datasets so they remain accurate, consistent, and accessible for quantitative research and simulation.
  • Develop and tune Python and SQL data-processing workflows across equities, options, futures, fixed income, ETFs, and foreign exchange.
  • Work with quantitative researchers, data architects, and infrastructure engineers to onboard datasets, strengthen data quality, and provide dependable research-ready data.
  • Implement monitoring, automation, and operational tools that support the reliability, performance, and scalability of the firm’s data platform.
  • Document pipelines and engineering practices while helping advance Trexquant’s research data infrastructure.

Requirements

  • Bachelor’s or master’s degree in computer science, engineering, mathematics, or a related quantitative discipline.
  • At least two years of data engineering experience in systematic trading, quantitative research, hedge funds, or financial technology.
  • Experience using Python and SQL to build large-scale data ingestion and ETL pipelines.
  • Strong Linux skills, including scripting, automation, and operation of production data-processing systems.
  • Thorough understanding of financial data across equities, options, futures, fixed income, ETFs, foreign exchange, and alternative datasets.
  • Experience handling market, tick, reference, and vendor data feeds, including normalization, validation, and quality control.
  • Familiarity with modern storage formats and technologies such as Parquet, Arrow, object storage, and columnar databases.
  • Strong communication and collaboration skills for working effectively with research and engineering teams.
  • Fluency in English.

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