Senior Quantitative Developer

Swish Analytics•San Francisco, CA

3d•$150,000

About The Position

Swish Analytics is a sports analytics, betting, and fantasy startup building the next generation of predictive sports analytics data products. We believe that oddsmaking is a challenge rooted in engineering, mathematics, and sports betting expertise; not intuition. We're looking for team-oriented individuals with an authentic passion for accurate and predictive real-time data who can execute in a fast-paced, creative, and continually-evolving environment without sacrificing technical excellence. Our challenges are unique, so we hope you are comfortable in uncharted territory and passionate about building systems to support products across a variety of industries and consumer/enterprise clients. You'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.

Requirements

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
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)
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)

Nice To Haves

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)
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++

Responsibilities

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
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)
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
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