Software Engineer II - Distributed Systems

Fanatics Inc•New York, NY

64d•$108,000 - $216,000•Remote

About The Position

At Fanatics Betting & Gaming (FBG), you'll join a team that turns raw sports data into rich, real-time product experiences across Sportsbook and Casino. We build the APIs, data pipelines, and service layers behind personalized recommendations, live search, and lightning-fast betting interfaces used by millions of fans. We treat AI as a standard tool in the developer toolbox (not a novelty). As a Software Engineer II, you'll apply our team's AI-assisted development practices (e.g., Claude Code, Cursor, GitHub Copilot) to ship faster without sacrificing correctness. You'll help design and scale backend services that bridge live sports data, internal content systems, and customer-facing applications, working closely with senior engineers, data scientists, and product managers. This role is ideal for someone growing from "solid backend contributor" to "reliable owner of real-time, data-driven services."

Requirements

2-5 years building backend systems or APIs in high-throughput or data-rich environments.
2+ years with Kotlin/Spring Boot; familiarity with reactive/non-blocking patterns is a plus.
Experience with event-driven systems (Kafka or similar) and at least one database (PostgreSQL or MongoDB) plus caching (Redis).
Solid grasp of microservices design and API fundamentals (REST/gRPC, pagination, auth, rate limiting, idempotency).
Comfort with observability practices (logs/metrics/traces), CI/CD, automated testing (unit/integration/contract), and Agile workflows.
Hands-on use of AI coding tools (Copilot, Cursor, Claude Code, etc.) to accelerate delivery-able to explain when they helped, when they hurt, and how you verified outputs.
Security hygiene: secrets handling, PII awareness, and adherence to data-usage rules when using AI tools.
Clear written and verbal communication in a remote-first environment; collaborative, ownership-oriented mindset.

Nice To Haves

Cloud experience (GCP/AWS) and/or Kubernetes; infrastructure as code familiarity.
Data-focused services: indexing pipelines, search APIs (OpenSearch/Elasticsearch), data quality checks, or real-time feeds.
Tooling/data stack exposure: Snowflake, Databricks, protobuf/Avro + Schema Registry, OpenTelemetry, k6/Vegeta for load testing.
Kotlin experience; protobuf/gRPC familiarity; knowledge of schema migration strategies and blue/green or canary deploys.
Background in startup-like environments or 0→1 initiatives; comfort iterating quickly with guardrails.
Interest in sports/gaming, fintech, Generative AI, recommendation systems, or personalization at scale.

Responsibilities

Design, develop, test, and deploy scalable backend services and REST/gRPC APIs powering search, live odds, box scores, and event tracking.
Use AI assistants throughout the SDLC (specs, scaffolding, tests, docs), while validating outputs via checklists, unit/integration tests, and code review.
Implement production-grade patterns: idempotent handlers, retries with jitter, backpressure, schema evolution, and safe migrations.
Collaborate with senior engineers to understand data pipelines, dependencies, SLAs/SLOs, and product requirements; break work into measurable increments.
Participate in on-call rotation; triage, debug, and resolve production issues using tracing, logs, and metrics; write post-incident follow-ups.
Contribute to architecture discussions, sprint planning, and design docs; propose pragmatic improvements that raise the team's delivery speed and safety.
Build observability into everything (RED/USE metrics, tracing, actionable alerts) and instrument latency/error/throughput KPIs.
Follow our AI-assisted PR (AIPR) process: add an AIPR note, include validation steps, attach tests/benchmarks, and tag the PR for metrics.
Help maintain prompt snippets and "gotchas" in the team's AI playbook; surface pitfalls (e.g., over-abstraction, missing edge cases, secret handling).
Develop domain fluency (sports event hierarchies, feeds, data quality) and how correctness/latency impact user trust and conversion.
Stay current with backend trends in distributed systems, cloud-native infra, data APIs, and AI-augmented engineering workflows.
Occasional travel to FBG offices or industry events for collaboration and team-building.