Data Engineer (CHINA)
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Before you apply
LocationBeijing, Beijing, China
Typefull-time
SeniorityMid
Experience2+ yrs
Company size201–500 people
PostedOct 8, 2026 · 3w ago
Verified live14h ago
At a glanceSummarised by Seekless from the posting.
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
Python
SQL
Linux
Parquet
Arrow
object storage
columnar databases
About the role
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
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Design, build, and maintain scalable ingestion pipelines for market, reference, tick, and alternative data from a diverse set of external vendors.
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Own the normalization, validation, storage, and lifecycle management of research datasets, ensuring data is accurate, consistent, and readily accessible for quantitative research and simulation.
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Develop and optimize Python- and SQL-based data processing workflows supporting multiple asset classes, including equities, options, futures, fixed income, ETFs, and FX.
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Partner with quantitative researchers, data architects, and infrastructure teams to onboard new datasets, improve data quality, and deliver reliable research-ready data.
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Build monitoring, automation, and operational tooling to ensure the reliability, performance, and scalability of the firm’s data platform.
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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