Data Engineer

New York, New York, United StatesHybridFull Time$200,000–$250,000 /yrPosted 2 months ago

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Role summary

A confidential quantitative trading firm is seeking a Data Engineer in New York City for a hybrid role. The Data Engineer will design, build, and maintain robust data infrastructure for research and trading platforms, working closely with quantitative researchers, traders, and software engineers. Key responsibilities include developing scalable data pipelines, optimizing workflows for performance and low latency, managing data lakes, warehouses, and streaming systems, and ensuring data integrity. The role requires a Bachelor's or Master's degree in a related field, 5+ years of experience in data or backend engineering, strong Python skills, experience with distributed data systems (Spark, Kafka, Flink), SQL/NoSQL databases, ETL/ELT pipelines, and cloud platforms (AWS, GCP, Azure).

Goldman Lloyds is a financial services executive search firm acting on behalf of the client.

Job Title:
Data Engineer

Location:
New York City, NY (Hybrid)

Company:
Confidential Quantitative Trading Firm

About Us

We are a high-performance quantitative trading firm leveraging cutting-edge technology, advanced research, and data-driven strategies to compete in global financial markets. Our team combines expertise in engineering, mathematics, and finance to build scalable systems that drive trading decisions in real time.

The Role

We are seeking a Data Engineer to design, build, and maintain robust data infrastructure that powers our research and trading platforms. You will work closely with quantitative researchers, traders, and software engineers to ensure high-quality, low-latency data pipelines.

Key Responsibilities

Design and implement scalable data pipelines for ingesting, processing, and storing large volumes of market and alternative data
Optimize data workflows for performance, reliability, and low latency
Develop and maintain data lakes, warehouses, and real-time streaming systems
Collaborate with quant researchers to deliver clean, structured datasets for modeling and backtesting
Ensure data integrity, monitoring, and validation across systems
Improve infrastructure automation and deployment processes

Required Qualifications

Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field
5+ years of experience in data engineering or backend engineering
Strong proficiency in Python
Experience with distributed data systems (e.g., Spark, Kafka, Flink)
Solid understanding of SQL and database design (relational and NoSQL)
Experience building and maintaining ETL/ELT pipelines
Familiarity with cloud platforms (AWS, GCP, or Azure)

Preferred Qualifications

Experience in financial services, trading, or time-series data
Knowledge of low-latency systems and high-frequency data processing
Experience with columnar storage formats (Parquet, ORC)
Understanding of containerization and orchestration (Docker, Kubernetes)

What We Offer

Competitive compensation, including bonus and profit-sharing
Comprehensive health, dental, and vision coverage
Opportunity to work on complex, high-impact systems
Collaborative, intellectually rigorous environment
Access to cutting-edge tools and technologies

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