Experienced Quantitative Developer at Swish Analytics
Job Description
Company Description
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.
Role Overview
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.
Core Responsibilities
Real-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 Qualifications
Domain 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)
...