Data Engineer

About Us :

Founded two decades ago and headquartered in Chicago, the DV Group of financial services firms has grown to more than 600 people across North America, Europe and Asia. Since becoming an independent proprietary trading firm in 2016, DV Trading has scaled rapidly using its own capital, strategies and risk management to provide liquidity to markets worldwide. DV Group affiliates today include two broker-dealers, a cryptocurrency market maker and a growing investment adviser.
DV Equities is the group's Asia-Pacific equities and futures market-making business, trading cash equities, ETFs, index futures and related products on exchanges across the region.

Overview:

We are looking for a Data Engineer to build and maintain the data infrastructure that powers our trading, research, and risk systems. You will work closely with quantitative researchers, traders, and software engineers to ensure that large-scale market data is clean, reliable, and easily accessible for both real-time trading and historical analysis. This is a hands-on role for someone who enjoys solving data problems in a fast-paced, collaborative environment.

Responsibilities:

• Design, build, and maintain scalable data pipelines for ingesting, processing, and storing large volumes of market and trading data
• Clean, validate, and enrich raw data to ensure high quality and consistency for research and production use
• Optimize data storage, query performance, and processing workflows to support low-latency and high-throughput requirements
• Collaborate with quantitative researchers and traders to understand data needs and deliver efficient data solutions
• Work with software engineers to integrate data pipelines into the firm's trading and research infrastructure
• Monitor data quality and pipeline health, troubleshoot issues, and implement improvements
• Document data flows, schemas, and processes to support team collaboration and knowledge sharing

Requirements:

• Bachelor's or Master's degree in Computer Science, Engineering, Information Systems, or a related field
• Strong proficiency in Python and SQL for data processing and analysis
• Experience with data pipeline tools and frameworks (e.g., Airflow, Kafka, Spark, or similar)
• Familiarity with relational and non-relational databases, as well as data lake/warehouse technologies
• Solid understanding of data modeling, ETL/ELT processes, and data quality best practices
• Experience working with large-scale datasets and optimizing performance
• Strong problem-solving skills and attention to detail
• Ability to communicate clearly and collaborate effectively with both technical and non-technical teams
• Experience in financial markets or trading data is a plus