Trexquant Investment LP
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.
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.
Competitive salary plus bonus based on individual and company performance.
Collaborative, casual, and friendly work environment.
PPO health, dental, and vision insurance premiums fully covered for you and your dependents.
Pre-tax commuter benefits.
Weekly company meals.
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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.
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.
Competitive salary plus bonus based on individual and company performance.
Collaborative, casual, and friendly work environment.
PPO health, dental, and vision insurance premiums fully covered for you and your dependents.
Pre-tax commuter benefits.
Weekly company meals.
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