Role OverviewYou'll architect and build the core trading systems that execute our fair value models across sports betting exchanges at scale. This is a systems engineering role focused on real-time decision-making, multi-venue orchestration, and low-latency execution under production constraints.
Core ResponsibilitiesReal-Time Trading Engine Architecture- Design event-driven trading systems that consume fair value models and market data to make sub-second execution decisions
- Build the core logic for comparing fair values against live market prices and determining when/where to trade
- Implement asynchronous order generation, submission, and cancellation workflows across multiple venues with different latency profiles
- Design state machines for order lifecycle management (pending, accepted, filled, cancelled, rejected) with proper event ordering and idempotency
Multi-Venue Execution & Routing- Build venue-specific integrations (WebSocket connections to Matchbook, Kalshi; REST API adapters for Betfair; FIX protocol handlers)
- Implement intelligent order routing that selects optimal venues based on liquidity, fees, latency, and position constraints
- Design coordination logic for managing orders across multiple venues when a single bet spans several platforms
- Handle venue-specific quirks (rate limiting, connection drops, partial fills, odds movement during submission)
Position & Risk Management Systems- Build real-time position tracking systems that aggregate exposure across all venues, markets, and event types
- Implement global liability management that enforces risk limits while maximizing capital utilization
- Design systems that detect and respond to position drift (when actual fills deviate from intended exposure)
- Create reconciliation engines that validate positions against venue reports and detect/resolve discrepancies
Data & Execution Infrastructure- Design data pipelines that ingest real-time market data from multiple feeds (WebSocket streams, REST polling, custom adapters) into low-latency in-memory stores
- Build efficient order book representation and query systems optimized for fast fair value lookups
- Implement message ordering and deduplication logic for ensuring consistent state across async operations
- Design persistent logging and event sourcing for order/trade auditing and post-incident analysis
Required QualificationsDomain Experience- 3+ years building production trading/market-making systems for betting syndicates, sharp groups, or exchanges
- Deep understanding of exchange vs. bookmaker dynamics and practical experience executing against both
- Hands-on experience integrating with real-time sports betting data feeds and exchange APIs
Technical Fundamentals- 3+ years of production Python with expert-level async/await, event loop, and concurrent execution skills
- Strong system design for distributed, real-time, event-driven systems
- Deep understanding of database transactions, consistency models, and state management under high throughput
- Experience with message streaming platforms (Kafka or equivalent) for order/execution event handling
- Proficiency with containerization (Docker), orchestration (Kubernetes), and cloud infrastructure (AWS, GCP)
Core Competencies- Ability to architect systems that make correct decisions under tight latency constraints
- Strong debugging skills for timing issues, race conditions, and event ordering problems
- Systematic problem-solving for production incidents in trading systems
- Pragmatic engineering decisions (when to accept latency vs. consistency tradeoffs)
Strongly Preferred- Experience building order management systems (OMS) or execution management systems (EMS)
- Background in low-latency or high-frequency trading system design
- Hands-on work with WebSocket real-time connections and connection resilience patterns
- Experience with FIX protocol or similar financial messaging standards
- Knowledge of multi-leg execution and cross-product coordination challenges
- Familiarity with market microstructure (order book dynamics, market impact, slippage models)
- Experience designing systems that respond to real-time market feedback (volatile prices, volume spikes)
Nice to Have- Contributions to trading infrastructure or market-making open-source projects
- Experience with Protobuf for efficient data serialization in latency-sensitive systems
- Exposure to blockchain/DeFi trading systems and AMM-style execution
- Knowledge of database CDC (Debezium) or event streaming architectures for audit/replay
- Background building resilience patterns (circuit breakers, backpressure, graceful degradation) in trading systems
- Experience working with Rust or C++
Base salary: starting at $170,000
Department Trading Analytics Role Trading Data Engineering Locations San Francisco, CA - Remote Remote status Fully Remote
