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Senior Kotlin Engineer - Backend - Kafka

Fanatics

$120,000 - $215,000
Sep 23, 2025
Remote, US
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Fanatics Betting & Gaming (FBG) is looking to solve the problem of developing and optimizing real-time systems for their sports betting platform, aiming to ship production code faster while maintaining exceptional quality standards by leveraging AI as a code collaborator.

Requirements

  • 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 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

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
  • 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)

Other

  • Self-motivated ability to have an idea, build it, and support it!
  • Identify and prevent common AI-generated code pitfalls (over-abstraction, missing edge cases, security vulnerabilities)
  • 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