Data Engineer
Company: Swish Analytics
Location: San Francisco, CA (Remote)
Type: Contract
Remote: Yes
Posted: 2026-07-22
About this role
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.
Job Description
The Swish Analytics team is seeking Data Engineers to have a direct impact on the infrastructure and delivery of our core consumer and enterprise data offerings as well as helping support our coverage of non-US sports. We’re a team passionate about accurate predictions and real-time data, and hope you find satisfaction in building new products with the latest and greatest technologies. This is a remote position.
Duties
- Support production systems and help triage issues during live sporting events
- Architect low-latency, real-time analytics systems including raw data collection, feature development and endpoint production
- Build new sports betting data products and predictions offerings
- Integrate large and complex real-time datasets into new consumer and enterprise products
- Develop production-level predictive analytics into enterprise-grade APIs
- Contribute to the design and implementation of new, fully-automated sports data delivery frameworks
Requirements
- BS/BA degree in Mathematics, Computer Science, or related STEM field
- Minimum of 2+ years of demonstrated experience writing production level code (Python)
- Proficiency in Python and SQL (preferably MySQL) Demonstrated experience with Airflow
- Demon...