Data Engineer

Company Overview

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 be building trading infrastructure for sports betting platforms, leveraging our proprietary fair value models to gain trading edge. This is a high-impact role where you'll architect and deploy low-latency systems that make real-time trading decisions.

Core Responsibilities

Trading Infrastructure Development

• Build and optimize market-making engines that consume real-time order book data and execute trades across multiple platforms
• Develop low-latency data ingestion pipelines for market data feeds (WebSocket, FIX protocol, REST APIs)
• Create real-time edge analysis systems that compare our fair values against live market prices to identify profitable opportunities
• Implement order management systems with robust error handling, position tracking, and risk controls

Production Systems & Operations

• Deploy and manage containerized applications on Kubernetes
• Build automated testing, deployment, and rollback procedures for trading systems
• Design and implement post-trade analysis tools to evaluate strategy performance
• Handle production incidents and optimize system performance under load

Required Qualifications

Technical Fundamentals

• 3+ years of production Python experience with strong async programming skills
• Deep understanding of Python's async/await patterns, event loops, and concurrent execution
• Experience building and maintaining production services in Linux environments
• Strong system design skills for distributed, real-time data processing systems
• Proficiency with SQL databases and data modeling

Infrastructure & Tools

• Experience with containerization (Docker) and orchestration (Kubernetes)
• Familiarity with message streaming platforms (Kafka preferred)
• Understanding of monitoring, logging, and observability practices
• Git workflows and CI/CD pipelines

Core Competencies

• Ability to write clean, maintainable, well-tested code
• Strong debugging skills and systematic problem-solving approach
• Comfortable working in a fast-paced environment with evolving requirements
• Self-directed with ability to make pragmatic technical decisions

Strongly Preferred

• Experience with trading systems, market-making, or order book dynamics
• Knowledge of sports betting markets or financial trading ecosystems (TradFi/Crypto)
• Experience with Protobufs, Argo Workflows, or similar tools in our stack
• Background in high/mid-frequency or low-latency system design
• Understanding of WebSocket protocols and real-time data streaming

Nice to Have

• Exposure to FIX protocol or other financial messaging standards
• Experience with Streamlit or similar tools for rapid dashboard development
• Knowledge of database CDC patterns (Debezium, etc.)
• Contributions to open-source trading or data infrastructure projects
• Experience working with Rust or C++

Base salary: Starting at $140,000 base

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.