Rust Engineer

Swish AnalyticsSan Francisco, CA
2d$155,000

About The Position

Swish Analytics is a sports analytics, betting and fantasy startup building the next generation of predictive sports analytics data products. We believe that oddsmaking is a challenge rooted in engineering, mathematics, and sports betting expertise; not intuition. We're looking for team-oriented individuals with an authentic passion for accurate and predictive real-time data who can execute in a fast-paced, creative, and continually-evolving environment without sacrificing technical excellence. Our challenges are unique, so we hope you are comfortable in uncharted territory and passionate about building systems to support products across a variety of industries and consumer/enterprise clients.

Requirements

  • Bachelor's Degree in Computer Science, Data Science or similar major
  • Minimum of 1 year of software engineering experience with Rust; 3 preferred
  • Minimum of 3 years of experience developing high-performance, scalable, and reliable production systems
  • Data Extraction, Wrangling and Analysis in Python
  • Strong SQL querying skills
  • Ability to work independently and take initiative

Nice To Haves

  • Experience with Apache Kafka and comparable systems
  • Exposure to the data science process and tech stack
  • Deep knowledge of football, basketball or baseball; including roster compositions of professional and college teams, general gameplay strategies, and typical in-game scenarios

Responsibilities

  • Develop high-performance and low-latency products to verify results and provide reliability for in-game play
  • Write Rust code, that's sophisticated, fast, and readable for complex, data science infrastructure
  • Design core, backend software components, and code primarily using Rust
  • Building internal and external tools to support Swish’s live trading platform
  • Source origins of data inaccuracies through data pipeline dependencies and python code base
  • Use extensive experience to build, test, debug, and deploy production-grade components
  • Proactively improve our Rust and Python codebase
  • Production model feature deep dives to explain project market lines
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