Staff Software Engineer - Remote

Overview

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 enterprise clients.

In Short

• Be the technical lead and owner for key products of our core backend applications.
• Perform code reviews and provide constructive feedback.
• Find optimizations in current applications, implement best practices, and ensure adherence to coding standards.
• Design and enhance both new and existing services.
• Work with globally distributed data.
• Participate in discussions and create and review architectural diagrams.
• Have a deep understanding of high-traffic, client-facing APIs and networking topology.

Requirements

• 8+ years of production software engineering and technical leadership experience.
• A creative mind for solving technically difficult problems in both standard and non-standard ways.
• Hands-on production experience with languages like NodeJS & Python.
• Hands-on production experience with databases, both relational and non-relational.
• Passionate about reliability, high availability, automation, observability, coding standards, and global scale.
• Strong communication skills working with technical and non-technical stakeholders.
• You have a BS or higher in CS/CE/SE or relevant experience.

Benefits

• Production experience with software engineering at a global level.
• Interest or experience with GO.
• Production experience with stream-processing paradigms like Kafka.
• Production experience with Docker and Kubernetes.
• Familiarity with sports data.
• Production experience working with low latent globally distributed data.
• Production experience developing, deploying and maintaining critical business applications.
• Understanding of various observability platforms.
• Knowledge of CI/CD.