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.

Our Core Engineering department is seeking a Senior Data Analyst to join our Data team in New York.

Responsibilities

Designing, building, and maintaining scalable batch and real-time data pipelines to ingest, cleanse, and normalize data from a wide variety of structured and unstructured sources (market data, web scrapes, vendors, alternative data)
Designing and productionizing AI/ML workflows for unstructured and semi-structured data, including document/entity extraction, classification, vendor-file parsing, news and filings processing, and alternative-data onboarding
Owning prompt/model selection, evaluation harnesses, human-in-the-loop review, and monitoring so AI-assisted feeds meet the firm’s accuracy and latency standards
Owning core investment data domains, designing and evolving data models for Security Masters, Corporate Actions, and Referential datasets across various asset classes (Equities, Futures, FX, Derivatives)
Implementing robust validation checks, anomaly detection, and reconciliation logic to ensure "zero-error" data delivery to trading systems
Applying statistical and ML-based methods to detect outliers, drift, and silent data breaks
Partnering directly with Data Scientists and Quants to understand their research needs, prototype data extraction methods (including AI-enabled approaches), and operationalize research signals into production-grade feeds
Managing the end-to-end process of onboarding new datasets, engaging with external vendors to understand data nuances, and integrating APIs
Assessing where AI can accelerate mapping, documentation, and QA without compromising data integrity

Qualifications

3+ years of professional experience in data engineering, preferably within the financial industry (Hedge Fund, Asset Manager, or FinTech)
Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, Physics, or a related quantitative discipline
Understanding of financial instruments, financial datasets from Bloomberg, S&P, LSEG
Experience with distributed computing frameworks like Spark or streaming technologies like Kafka
Proficiency in C++, Java, or Rust is a strong plus
Experience with retrieval-augmented generation, vector search, fine-tuning or distillation, LLM evaluation frameworks, or agentic workflows for data operations
Prior work applying AI to financial documents, corporate actions, or alternative data
Programming Mastery: Expert-level proficiency in Python (including Pandas, NumPy, and async frameworks) and advanced SQL (complex queries, window functions, performance tuning)