Product Engineer

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Swish Analytics

11 - 50 employees

Founded 2014

🎲 Gambling

🤖 Artificial Intelligence

🤝 B2B

💰 $6.9M Series B - Swish Analytics on 2019-05

Gambling • Artificial Intelligence • B2B

Swish Analytics is a machine-learning driven sports-odds and predictive-data company that builds a real-time odds origination engine for sportsbooks and fantasy platforms. It provides hyper-accurate player prop pricing, match and team markets, in-play and pre-game projections, and automated bet-lifecycle management to power bookmaking, bet builders, parlays, and micro-markets. Swish positions itself as a B2B provider focused on automation, accuracy, and scalable odds infrastructure for the sports betting industry.

📋 Description

• Build, test, debug, and deploy production-grade components and services powering our real-time sports betting and analytics products • Own the deployment and operation of your own services on Kubernetes within a larger platform — this is service ownership within a shared ecosystem, not cluster administration or a DevOps function • Proactively improve and extend our Rust codebase • Trace and resolve the root causes of data inaccuracies across pipeline dependencies and our Python codebase • Set up and maintain alerting, monitoring, and observability for the services you own • Build and maintain the shared frameworks and services that let our models scale in production, and accelerate their adoption across teams • Apply large-scale data processing techniques to build scalable, innovative sports betting products • Examine and improve the integration and scaling of our real-time operations, simulations, and experiments • Develop solutions for open-ended problems where information is incomplete and no precedent exists

🎯 Requirements

• Bachelor's degree in Computer Science or a related technical field • 5–7 years building and shipping production software • Advanced Python experience (Rust experience strongly preferred) • Comfortable owning your own services in a Kubernetes environment • Experience setting up alerting and monitoring systems • Experience with Kafka (or a comparable streaming / event platform) • Proficiency with SQL • Experience with source control (GitHub) and related CI/CD processes • Experience working in AWS environments • Track record of technical leadership and partnering across teams on complex, ambiguous problems • Strong communication skills with both technical and non-technical audiences.

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