Trexquant is seeking an experienced Data Engineer to build and maintain the core data infrastructure that powers our quantitative research platform. This role is responsible for owning the ingestion, normalization, storage, and ongoing maintenance of large-scale financial and alternative datasets from hundreds of global vendors.
The successful candidate will develop scalable data pipelines that transform raw vendor feeds into clean, consistent, research-ready datasets for systematic researchers and simulation platforms. Working closely with quantitative researchers, data platform engineers, and infrastructure teams, this person will ensure that market, reference, and alternative data is accurate, reliable, and readily accessible across asset classes including equities, options, futures, fixed income, ETFs, and foreign exchange.
This is an ideal opportunity for an engineer who enjoys solving complex data engineering challenges in a research-driven environment where data quality, scalability, and performance directly impact alpha generation.
Responsibilities- Design, build, and maintain scalable ingestion pipelines for market, reference, tick, and alternative data from a diverse set of external vendors.
- Own the normalization, validation, storage, and lifecycle management of research datasets, ensuring data is accurate, consistent, and readily accessible for quantitative research and simulation.
- Develop and optimize Python- and SQL-based data processing workflows supporting multiple asset classes, including equities, options, futures, fixed income, ETFs, and FX.
- Partner with quantitative researchers, data architects, and infrastructure teams to onboard new datasets, improve data quality, and deliver reliable research-ready data.
- Build monitoring, automation, and operational tooling to ensure the reliability, performance, and scalability of the firm's data platform.
- Document data pipelines and engineering best practices while contributing to the ongoing evolution of Trexquant's research data infrastructure.
Requirements
- Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or a related quantitative field.
- at least 2 years of data engineering experience within a systematic trading, quantitative research, hedge fund, or financial technology environment.
- Python and SQL development experience in building large-scale data ingestion and ETL pipelines.
- Strong Linux experience, including scripting, automation, and operating production data processing systems.
- Deep knowledge of financial data across multiple asset classes, including equities, options, futures, fixed income, ETFs, FX, and alternative datasets.
- Experience working with market data, tick data, reference data, and vendor data feeds, including normalization, validation, and quality control.
- Familiarity with modern data storage formats and technologies such as Parquet, Arrow, object storage, and columnar databases.
- Strong communication and collaboration skills, with the ability to work effectively alongside researchers and engineering teams.
- Fluent in English
Skills Required
- Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or a related quantitative field
- At least 2 years of data engineering experience in systematic trading, quantitative research, hedge fund, or financial technology environments
- Python and SQL development experience building large-scale data ingestion and ETL pipelines
- Strong Linux experience, including scripting, automation, and operating production data processing systems
- Deep knowledge of financial data across equities, options, futures, fixed income, ETFs, FX, and alternative datasets
- Experience with market data, tick data, reference data, and vendor data feeds, including normalization, validation, and quality control
- Familiarity with Parquet, Arrow, object storage, and columnar databases
- Strong communication and collaboration skills
- Fluent in English
What We Do
Being a quantitative finance firm that uses Machine Learning (ML) to create multi-asset portfolios and seek profit from the market, Trexquant has continuously improved its investment and research platform since starting operations, leveraging new and emerging technologies. Trexquant uses rigorous quantitative methods to create multi-asset portfolios in global markets. To do this, Trexquant develops trading signals using its vast and continuously growing collection of data variables used as inputs for more complex trading models called Strategies. The result is an ever-growing and adapting engine built from thousands of intricate models and tens of thousands of signals, tailor-made with the goal to outperform the market during any condition. Capital is managed across 5,500+ cash equity positions across the United States, Europe, Japan, Australia, and Canada.
