Experienced Quantitative Developer

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)

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

Swish Analytics is an Equal Opportunity Employer. All candidates who meet the qualifications will be considered without regard to race, color, religion, sex, national origin, age, disability, sexual orientation, pregnancy status, genetic, military, veteran status, marital status, or any other characteristic protected by law. The position responsibilities are not limited to the responsibilities outlined above and are subject to change. At the employer’s discretion, this position may require successful completion of background and reference checks.