Backend and Data Engineer - Risk and Trading

Summary

Join Sleeper, a rapidly growing sports fan platform, as a backend/data engineer. You will design and implement real-time data pipelines using Python, SQLX, and BigQuery, build and operate backend microservices, and architect a feature store. Responsibilities include integrating streaming and pub/sub systems, hardening CI/CD, and partnering with data scientists. You will own projects from ideation to production, ensuring data integrity in a real-money system. The ideal candidate has 4+ years of experience in backend and data engineering with Python and strong SQL skills. Sleeper offers competitive salary and stock options, comprehensive health insurance, 401k, flexible working hours, and remote-first culture.

Requirements

• 4+ years of backend and/or data-engineering experience with Python (FastAPI, Django, or similar) plus strong SQL chops
• Proven track record designing distributed systems that handle high-volume, low-latency workloads
• Experience with stream processing (Kafka, Pub/Sub, Flink or Beam) and warehouse technologies (BigQuery, Snowflake, or Redshift)
• Comfortable—or eager to learn—Elixir/Erlang and other functional stacks
• Pragmatic engineer who anticipates edge cases and failure modes in financial or real-time environments
• Calm, methodical communicator during incidents; you thrive under pressure

Responsibilities

• Design & implement real-time data pipelines (Python, SQLX, BigQuery) that ingest odds, bets, and exposure signals with low latency
• Build and operate backend micro-services for pricing, settlement, and limit management that expose clean gRPC/REST contracts to our Elixir cluster
• Architect a feature store that serves models in production and supports ad-hoc analytics with strong data governance
• Integrate streaming & pub/sub systems (Kafka / Google Pub/Sub) with caching layers (Redis, ScyllaDB) to guarantee consistency under high throughput
• Harden CI/CD, observability, and incident-response runbooks so data integrity issues surface before they hit users
• Partner with data scientists to deploy and monitor models end-to-end, from training to online inference
• Own projects from ideation through production, balancing delivery speed against the safety demands of real-money systems

Preferred Qualifications

• Prior work with ScyllaDB/Cassandra, ClickHouse, or other wide-column / OLAP stores
• Hands-on experience with Kubernetes, Terraform, GitLab CI, and observability stacks (Prometheus, Grafana, OpenTelemetry)
• Familiarity with Rust, Go, or C++ for ultra-low-latency components
• Knowledge of risk modeling, market-making algorithms, or sports-betting infrastructure

Benefits

• Competitive salary and stock options
• Comprehensive health, dental, and vision insurance
• 401(k)
• Flexible working hours and remote-first culture
• Clear paths for career growth and leadership