Experienced Quantitative Developer - Swish Analytics

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.

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 opti