Swish Analytics

Analyst

• Manage client risk, ensuring the highest margins on Swish products through +EV decisions
• Manage and oversee depth chart accuracy, making time-sensitive adjustments in both prematch & in-play settings
• Strategically research & track verified news sources, being the first to uncover important news and understand the overall impact on betting markets
• Independently analyze betting trends in a scientific process, turning data into quantifiable trading actions

Trading Analyst

Full-stack Engineer

• Design, build, and optimize low latency, highly available core API services
• Build backend applications while working cross-functionally with teams across the organization
• Assist in developing a platform and SDK for internal teams to build their services
• Work with the Swish infrastructure team to optimize the existing API service architecture and strategy
• Participate in code reviews and knowledge sharing with teams and team members
• Influence the technical direction of distributed services at Swish

Senior Software Engineer

Customer Success Manager

• Serve as a key point of contact for B2B clients, managing inbound inquiries via Slack, email, and video calls
• Develop deep expertise in Swish’s products, data, and workflows to effectively support client needs
• Troubleshoot client issues in real time, translating technical concepts into clear, actionable guidance
• Proactively identify client pain points, usage gaps, and opportunities to improve retention and satisfaction
• Monitor client performance and engagement, ensuring successful onboarding and ongoing adoption

Customer Success Associate

Software Engineer

• 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

Experienced Quantitative Developer

DevOps Engineer

• Use your experience to develop, maintain and implement Kubernetes clusters at scale that are ready for heterogeneous and elastic workloads
• Work closely with Data Science and Data Engineering teams to implement, optimize and scale systems on Kubernetes using CI/CD, automation tools and scripting languages
• Help Data Science and Data Engineering develop and implement specialized infrastructure to deliver tools, software, and platforms that improve the reliability and scalability of capabilities
• Pioneer, implement, and encourage best practices for software deployment and code management through automation and education
• Monitor the system and respond to incidents to maintain system SLO/SLA, review and follow up production incidents
• Provide on call work as needed

Data Analyst

• Work closely with Data Scientists and Engineers to diagnose and treat data pipeline integrity issues
• Detect data inaccuracies such as missing, out of range or otherwise incorrect on-field data
• Source origins of data inaccuracies through data pipeline dependencies and python code base
• Define data validation tests to flag future game errors
• Research accurate roster active statuses, primary positions and game participation
• Validate data changes after logic updates
• Production model feature deep dives to explain project market lines
• Clearly document findings
• Develop intimate familiarity with existing databases and construct metadata references
• With guidance, support lead Data Scientists in feature development and model analysis

Sports Data Analyst

• Be the technical lead and owner for key products of our core backend applications.
• Perform code reviews and provide constructive feedback.
• Find optimizations in current applications, implement best practices, and ensure adherence to coding standards.
• Design and enhance both new and existing services.
• Work with globally distributed data.
• Participate in discussions and create and review architectural diagrams.
• Have a deep understanding of high-traffic, client-facing APIs and networking topology.