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Tower Research Capital

Trades financial markets with computer models

Senior Data Analyst

Data EngineeringConfirmed live in the last 24 hours
Full-timeNew York, NY, USA

About the Job

Requirements

  • At least 3 years of professional experience in data engineering, preferably within a hedge fund, asset manager, or fintech company.
  • A bachelor's or master's degree in Computer Science, Engineering, Mathematics, Physics, or a related quantitative discipline.
  • Understanding of financial instruments and financial datasets from Bloomberg, S&P, or LSEG.
  • Experience with distributed computing frameworks such as Spark or streaming technologies such as Kafka.
  • Expert proficiency in Python, including Pandas, NumPy, and asynchronous frameworks, and advanced SQL, including complex queries, window functions, and performance tuning.
  • Hands-on production experience applying AI or machine learning to data problems such as natural language processing, information extraction, classification, or large language model processing of unstructured data.
  • Ability to evaluate model quality using precision/recall, error analysis, and gold-set design; manage failure modes; and deliver reliable production pipelines.
  • Familiarity with AI libraries and APIs such as scikit-learn, PyTorch, Hugging Face, or large language model APIs.
  • Strong hands-on experience with workflow orchestration tools such as Airflow, Dagster, or similar tools.
  • Proven experience building data platforms on AWS or GCP and using modern data warehouses such as Snowflake, BigQuery, or Databricks.
  • Understanding of dimensional modeling, slowly changing dimension strategies, data partitioning, and very large database design principles.
  • Experience with Linux and Windows operating systems, NFS, S3, and work management platforms such as JIRA and Confluence.
  • Ability to communicate technical and AI-related concepts, including limitations and risks, effectively to non-technical traders and researchers.

Responsibilities

  • Design, build, and maintain scalable batch and real-time data pipelines to ingest, cleanse, and normalize structured and unstructured sources, including market data, web scrapes, vendors, and alternative data.
  • Design and productionize AI and machine learning workflows for unstructured and semi-structured data, including document and entity extraction, classification, vendor-file parsing, news and filings processing, and alternative-data onboarding.
  • Own prompt and model selection, evaluation harnesses, human-in-the-loop review, and monitoring to ensure AI-assisted feeds meet firm accuracy and latency standards.
  • Own core investment data domains and design and evolve data models for Security Masters, Corporate Actions, and Referential datasets across equities, futures, foreign exchange, and derivatives.
  • Implement validation checks, anomaly detection, and reconciliation logic to deliver accurate data to trading systems.
  • Apply statistical and machine-learning methods to detect outliers, drift, and silent data breaks.
  • Partner with Data Scientists and Quants to understand research needs, prototype data extraction methods, including AI-enabled approaches, and operationalize research signals into production-grade feeds.
  • Manage end-to-end onboarding of datasets, engage with external vendors to understand data nuances, and integrate APIs.
  • Assess where AI can accelerate mapping, documentation, and quality assurance without compromising data integrity.

Desired Qualifications

  • Proficiency in C++, Java, or Rust is a strong plus.
  • Experience with retrieval-augmented generation, vector search, fine-tuning or distillation, large language model evaluation frameworks, or agentic workflows for data operations.
  • Prior work applying AI to financial documents, corporate actions, or alternative data.

About the Company

Tower Research Capital

Trades financial markets with computer models

Tower Research Capital trades financial instruments with computer models and supplies liquidity, or ready buying and selling prices, to other market participants. Its researchers test strategies against historical data before deploying them through a platform built largely in-house. The platform combines market data, order routing and risk controls, letting traders act on signals across stocks, futures, options and currencies. It earns trading profits by taking positions for its own account, and its liquidity businesses trade directly with counterparties rather than managing their investments. Tower was founded in 1998 and is headquartered in New York City.

Company size
1,001-5,000
Company stage
Not listed
Total funding
$116.1M
Headquarters
New York City, New York
Founded
1998

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