Software Engineer III - Backend

At Fanatics Betting & Gaming (FBG), a core division of Fanatics' mission to establish the ultimate end-to-end digital sports platform, we're crafting a world where every aspect of a sports fan's passion is catered to.

This Software Engineer III (Distributed Systems - Kotlin, Java, Kafka) role entrusts you with significant ownership over the development and optimization of real-time systems that power our sports betting platform. You'll lead by example, establishing best practices for AI-assisted development while building systems that handle millions of events daily.

On the engineering side, we're pioneering the use of AI as a code collaborator. We need engineers who have gone beyond experimentation-who actively use Claude Code, Cursor, GitHub Copilot, or similar tools to ship production code faster while maintaining exceptional quality standards. If you have strong opinions about AI-augmented development workflows backed by real experience, we want to talk with you.

Responsibilities

• Design, build, and optimize real-time betting systems handling 10K+ events per second
• Ensure 99.999% uptime for customer-facing services through robust error handling and failover strategies
• Optimize database queries, caching strategies, and event streaming pipelines for sub-100ms response times
• Full feature ownership: spec writing * implementation * deployment * monitoring * iteration based on metrics
• Self-motivated ability to have an idea, build it, and support it!
• Leverage AI tools to accelerate development velocity while maintaining code quality standards
• Establish and document team standards for AI tool usage (prompt patterns, code review checklists, validation strategies)
• Measure and report on AI tool ROI through concrete metrics (PR velocity, bug rates, test coverage)
• Identify and prevent common AI-generated code pitfalls (over-abstraction, missing edge cases, security vulnerabilities)

• 7+ years building and deploying scalable, high-performance production applications
• Kotlin and/or Java: 3+ years building production microservices
• Spring Boot: Deep understanding of reactive programming and non-blocking I/O
• PostgreSQL: Complex query optimization, indexing strategies, and migration management
• Kafka: Event streaming patterns, partition strategies, and consumer group management at scale
• Redis/Redis Pub/Sub: Building real-time features supporting hundreds of thousands of concurrent users
• Demonstrated experience using AI tools (Claude Code, Cursor, Copilot, etc.) to ship production code
• Can articulate specific examples of workflow improvements (e.g., "reduced boilerplate generation time by 40%")
• Has developed personal strategies for validating AI-generated code and identifying common pitfalls
• Can compare at least 2-3 AI tools with concrete pros/cons from actual usage
• Strong grasp of software design principles (SOLID, DRY) and testing methodologies (TDD, BDD)
• Track record of introducing tools or processes that measurably improved team velocity
• Experience with observability and monitoring in distributed systems
• Can write clear technical documentation and present architecture decisions to non-technical stakeholders
• Team-first mentality with willingness to jump in wherever needed
• Actively experiments with and optimizes personal development workflow
• Strong written and verbal communication skills
• Self-directed problem solver who thrives in ambiguous situations

Preferred Qualifications

• Experience in sports betting industry or genuine interest in sports
• Previous experience in high-growth startup environments
• Contributions to open-source projects or technical community
• Experience with real-money transaction systems and regulatory compliance
• Background in building developer tools or improving engineering productivity
• Experience leading without authority and influencing technical direction across teams

Note on AI Workflow: We're serious about AI-augmented development. During interviews, be prepared to:

• Demo your current AI-assisted workflow
• Discuss specific examples of how you've used AI to solve complex problems
• Share your strategies for ensuring AI-generated code meets production standards