Tower Research Capital
Financial Services
DataEngineer
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Data Engineer at Tower Research Capital. Build data layer. Own data layer”
Industry & Context.
Problem solving
What They're Looking For.
Must Have
25+ year track record
What You'll Do.
Develop scalable pipelines
Ensure high-quality inputs
Partner with researchers
Enable alpha development
Establish data validation standards
Establish data monitoring standards
Establish data documentation standards
How You'll Work.
Team & Collaboration
Quant researchers
Full Job Description
Tower Research Capital is a leading quantitative trading firm founded in 1998. Tower has built its business on a high-performance platform and independent trading teams. We have a 25+ year track record of innovation and a reputation for discovering unique market opportunities. Tower is home to some of the world’s best systematic trading and engineering talent. We empower portfolio managers to build their teams and strategies independently while providing the economies of scale that come from a large, global organization. Engineers thrive at Tower while developing electronic trading infrastructure at a world class level. Our engineers solve challenging problems in the realms of low-latency programming, FPGA technology, hardware acceleration and machine learning. Our ongoing investment in top engineering talent and technology ensures our platform remains unmatched in terms of functionality, scalability and performance. At Tower, every employee plays a role in our success. Our Business Support teams are essential to building and maintaining the platform that powers everything we do — combining market access, data, compute, and research infrastructure with risk management, compliance, and a full suite of business services. Our Business Support teams enable our trading and engineering teams to perform at their best. At Tower, employees will find a stimulating, results-oriented environment where highly intelligent and motivated colleagues inspire each other to reach their greatest potential. Responsibilities : Building and owning the end-to-end data layer for systematic equities trading (from raw vendor data to research-ready datasets) Developing scalable pipelines for ingestion, cleaning, normalization, and point-in-time accurate historical data Handling data nuances including revisions, biases, and inconsistencies to ensure high-quality inputs for research Partnering closely with quant researchers to enable alpha development through reliable datasets Establishing data v
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