Swish Analytics Tech Stack

Sports betting odds and risk management platform powered by predictive analytics

Spectator Sports San Francisco, California 11–50 employees Founded 2014 Privately Held

Swish Analytics operates a data-engineering-heavy sports analytics platform for U.S. sports betting, structured around odds origination, risk management, and trading software. The tech stack—Python, Kafka, Airflow, Kubernetes, Redshift, BigQuery, and multi-cloud compute (AWS, GCP, Azure)—reflects a mature data pipeline built for low-latency predictions. Hiring is dominated by data roles (16 of 26 active postings), with open positions spanning engineering, research, and product, signaling active expansion of both predictive capability and product surface.

Tech Stack 35 technologies

Core StackAWS Python GitHub Kubernetes MySQL Apache Airflow Superset Trino Tableau Looker Power BI Kafka Rust Redshift Azure Data Factory BigQuery PostgreSQL Redis CloudWatch SQL REST API Git CI/CD Athena Streamlit Dash GCP Azure Valkey Performance Insights+4 more

What Swish Analytics Is Building

◆Challenges

Oddsmaking challenge
Model performance improvement
Data pipeline inaccuracies
Improving model performance through experimentation
Supporting products across industries
Kubernetes development and optimization
Low latency predictions
Scalable sports betting products
Scaling global workforce
Compliance across multiple jurisdictions

▲Active Projects

Develop core algorithms for sports betting products
Deploy new models with data engineering
Real-time monitoring systems for latency and execution metrics
Low-latency real-time analytics system
Proof-of-concepts
Sports data models
Feature engineering for soccer
Model deployment pipeline
Sports betting data product
Automated sports data delivery framework

Hiring Activity

Accelerating25 roles · 15 in 30d

Department

Data

Engineering

Finance

Product

Research

Seniority

Senior

Mid

Staff

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About Swish Analytics

Swish Analytics builds predictive analytics and trading software for the core four U.S. sports. The platform handles odds origination, risk management, and execution—functions the company frames as engineering and mathematics problems rather than intuitive judgment. Founded in 2014 and headquartered in San Francisco, Swish operates at the intersection of sportsbooks, betting operators, and trading desks. Current work centers on real-time model deployment, feature engineering (especially for soccer), sports data modeling, and automated data delivery frameworks—all constrained by latency and model-performance requirements. The company is scaling globally, hiring across the U.S., U.K., Spain, and Malta.

HeadquartersSan Francisco, California

Company Size11–50 employees

Founded2014

Hiring MarketsSpain, United States, United Kingdom, Malta

Frequently Asked Questions

What tech stack does Swish Analytics use?

Python, SQL, Kubernetes, Apache Airflow, Kafka, Redshift, BigQuery, AWS, GCP, Azure, Trino, and Athena. Visualization tools include Tableau, Looker, Superset, Streamlit, and Dash.

Where is Swish Analytics hiring?

United States, United Kingdom, Spain, and Malta. The company is scaling globally across these four countries.