Backend Engineer, Sleeper

• 4+ years of backend engineering experience with a strong focus on Python (FastAPI, Django, or similar)
• Proven ability to design and operate distributed systems handling high throughput and low latency
• Experience with message queues/streams (Apache Kafka, Google Pub/Sub) and caching layers (Redis)
• Comfortable interacting with Elixir/Erlang or other functional stacks, or eager to learn
• Pragmatic engineer who thinks critically about edge cases and failure modes, especially in financial or real‑time systems
• Calm, methodical, and communicative when incidents occur; you thrive under pressure
• Preferred Skills (Nice to Have)Prior work with ScyllaDB/Cassandra or other wide‑column distributed databases
• Familiarity with Rust, Go, or C++ for high‑performance components
• Knowledge of risk modeling, market‑making algorithms, or sports‑betting infrastructure
• Hands‑on experience with Kubernetes, GitLab CI, and infrastructure‑as‑code tools (Terraform)
• Deep understanding of observability: Prometheus, Grafana, OpenTelemetry

Desirable

• Sleeper is a rapidly growing platform for sports fans with messaging and gameplay at its core
• While much of our existing infrastructure is Elixir‑driven, this role will focus on Python services that integrate tightly with Elixir, GCP, and our distributed databases
• You will collaborate with product, data, and frontend teams to ship user‑facing features that are fair, fast, and resilient
• Design & implement Python backend services that power real‑time risk management and trading operations
• Build scalable infrastructure for pricing engines, market creation, settlement, and exposure management
• Integrate with our Elixir services via gRPC/REST, Kafka, and Redis to ensure consistency and low‑latency performance
• Architect distributed systems on Google Cloud Platform (GCP) using Pub/Sub, Cloud Run/Kubernetes, and BigQuery
• Develop robust monitoring, alerting, and CI/CD pipelines to ensure high availability and quick incident response
• Collaborate with data scientists on model deployment and optimize end‑to‑end latency from pricing to settlement
• Own projects from ideation through production, making thoughtful trade‑offs in ambiguous, high‑stakes environments