[Hiring] Experienced Quantitative Developer @Swish Analytics

Role Description

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.

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).

Requirements

• 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++.

Benefits

• Base salary starting at $170,000.

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.