About the Role
Join our data engineering team as an intern and help build the data infrastructure that powers our systematic trading strategies. You'll work on high-throughput data pipelines that ingest, process, and serve financial market data to our research and production systems.
This role offers a unique opportunity to learn about distributed systems, real-time data processing, and the technical challenges of working with financial data at scale. You'll collaborate with quantitative researchers and software engineers to ensure our data infrastructure is reliable, performant, and maintainable.
We're looking for students with strong programming fundamentals and an interest in data systems. Whether you're studying computer science, data science, or a related field, this internship will give you hands-on experience with production-grade data engineering.
What You'll Do
- • Build and maintain data pipelines for ingesting market data from multiple sources (exchanges, data vendors, alternative data providers)
- • Implement data quality checks and monitoring systems to ensure data integrity
- • Work with time-series databases and data warehouses to optimize storage and retrieval
- • Develop ETL processes for cleaning, transforming, and aggregating financial data
- • Create tools and APIs for researchers to access and query data efficiently
- • Assist in migrating legacy data systems to modern cloud infrastructure
- • Document data schemas, pipelines, and best practices for the team
Requirements
- • Currently pursuing a Bachelor's or Master's degree in Computer Science, Data Science, Information Systems, or related technical field
- • Proficiency in Python and SQL
- • Understanding of data structures, algorithms, and software engineering fundamentals
- • Experience working with databases (PostgreSQL, MySQL, or similar)
- • Familiarity with data processing libraries (Pandas, NumPy) and data workflows
- • Strong problem-solving skills and attention to detail
- • Ability to write clean, maintainable, and well-documented code
Nice to Have
- • Experience with distributed systems or stream processing frameworks (Kafka, Spark, Flink)
- • Knowledge of time-series databases (InfluxDB, TimescaleDB, KDB+)
- • Familiarity with cloud platforms (AWS, GCP, Azure) and infrastructure as code
- • Experience with containerization (Docker) and orchestration (Kubernetes)
- • Understanding of API design and microservices architecture
- • Prior exposure to financial data or trading systems
- • Experience with monitoring and observability tools (Prometheus, Grafana)
What You'll Learn
- • How to build robust, scalable data pipelines for financial applications
- • Best practices for handling large-scale time-series data
- • Real-world experience with distributed systems and cloud infrastructure
- • Data quality management and validation techniques
- • How data engineering supports quantitative research and trading operations
Ready to Apply?
Submit your resume, transcript, and a brief cover letter. Include links to any relevant projects or GitHub repositories.
Apply NowQuick Facts
- Location
- NJ/Remote
- Type
- Internship