Data Engineer, Multi-Asset Arbitrage

Balyasny Asset Management (BAM) is a diversified global investment firm founded in 2001 by Dmitry Balyasny, Scott Schroeder, and Taylor O'Malley. With over $32 billion in assets under management, BAM employs more than 2,000 people across 23 offices in the U.S. and Canada, Europe, the Middle East, and Asia. The firm's investment teams span five strategies, including Equities Long/Short, Fixed Income & Macro, Commodities, Multi-Asset Arbitrage, and Systematic. Balyasny's mission is to deliver to its investors absolute, uncorrelated returns in all market environments.

The team is responsible for building, owning, and supporting a world-class data platform for our portfolio managers and their teams. We are building the suite of core components that will underpin the offering of this team for years to come. We are looking for an experienced and eager engineer to join our team in supporting this mandate.

Role Overview:
• Design, build, and grow the data plane for back-testing and other data-intensive applications
• Handle Petabyte-level ingestion, ETL, data quality, storage, backfills, and consumption/API's
• Work closely with quantitative engineers and researchers
• Collaborate in a global team environment to understand, engineer, and deliver on business requirements
• Strike a balance along the dimensions of feasibility, stability, scalability, and time-to-market when delivering solutions

Qualifications & Requirements:
• 5+ years of work experience in a data-intensive capacity, building back-testing infrastructure or similar initiatives.
• Demonstratable expertise in SQL and databases, especially timeseries and big data stores.
• Strong skills in Python/Rust/C++ and at least one data-manipulation library/framework (e.g., Pandas, Polars, Dask, Vaex, PySpark)
• Strong debugging skills at all levels of the application stack and proven problem-solving ability
• Experience architecting and building data platforms / ETLs, ideally batch as well as streaming, data lake/warehouse/lakehouse patterns
• Experience with column-oriented data storage and serialization formats such as Parquet/Arrow
• Experience with code optimization and performance tuning
• Strong knowledge of the data components used in distributed applications (e.g., Kafka, Redis, or other messaging/caching tools)
• Excellent communication skills

Additional experience in the following areas is a plus:
• Prior experience with ClickHouse, Snowflake, or similar
• Work with financial instruments / software in areas such as research, risk management, portfolio management, reconciliation, order management, etc.
• Experience building application-level code e.g., REST APIs to expose business logic
• Prior usage of tooling such as Prometheus, Grafana, Sentry, etc. for distributed tracing and monitoring metrics
• Experience with distributed stateful stream processing (e.g., Kafka Streams, Flink, Arroyo)