Experienced Quantitative Developer

🕒 Maio 13

🏄 California – Remoto

🏄 California – Remote

💵 $170.000 / ano

⏰ Tempo Integral

🟡 Pleno

🟠 Sênior

🖥 Engenheiro de Software

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Swish Analytics

SiteLinkedInTodas as Vagas

11 - 50 funcionários

Fundada em 2014

🎲 Jogos de Azar

🤖 Inteligência Artificial

🤝 B2B

💰 $6.909.110 Series B - Swish Analytics em 2019-05

Gambling • Artificial Intelligence • B2B

A Swish Analytics é uma empresa de dados preditivos e probabilidades esportivas orientada por machine learning que desenvolve um mecanismo em tempo real de criação de probabilidades para casas de apostas e plataformas de fantasia. A empresa oferece precificação de props de jogadores com alta precisão, mercados de partidas e equipes, projeções em tempo real e pré-jogo, e gestão automatizada do ciclo de vida de apostas para alimentar o processo de bookmaking, construtores de apostas, parlays e micro-mercados. A Swish se posiciona como um fornecedor B2B focado em automação, precisão e infraestrutura escalável de probabilidades para a indústria de apostas esportivas.

Descrição

• Architect and build the core trading systems that execute our fair value models across sports betting exchanges at scale • 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

🎯 Requisitos

• 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)