Senior Data Engineer
Belvedere Trading is a proprietary trading firm and market maker founded in Chicago. It develops proprietary trading models, software, and risk-management systems, and operates across Chicago, Boulder, New York, and Singapore.
About Belvedere Trading, LLC.
Belvedere Trading is a proprietary trading firm and market maker specializing in financial markets, including options, indices, commodities, equities, and equity strategies. The company uses proprietary trading models, software platforms, quantitative research, and risk-management capabilities to identify market inefficiencies and execute trading strategies. It also provides capital, infrastructure, operational support, and strategic guidance to backed trading groups and partners, and may acquire established trading teams or their intellectual property. The company recruits and trains quantitative traders, researchers, software engineers, and operations professionals through its Belvedere Trading University program.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
Work directly with trading desks to improve trading performance by ensuring data availability, breadth, and reliability. The role covers ETL pipelines, data warehouse architecture, data quality automation, analytical visualization, and scalable architecture for petabyte-scale trading data.
Requirements
- Experience designing and developing big data warehouses and ETL pipelines.
- Proven knowledge of SQL and Python.
- Ability to integrate data across RDBMS, NoSQL, and Time Series platforms.
- Experience with Kafka, Kinesis, Pulsar, Flink, or Spark is a plus.
- Proficiency writing high-performance, scalable BigQuery queries.
- Excellent verbal and written communication, analytical, and problem-solving skills.
Responsibilities
- Work with traders to understand analytic needs and improve trading performance.
- Design and maintain modular data architecture for future analysis.
- Ensure high data quality for analytical trading datasets.
- Maintain data standards involving modular design, testing, and documentation.
- Maintain visual standards involving modular design, testing, and documentation.
- Ensure technical solutions are simple and scalable for future use cases.