[Remote] Data Engineer at Swish Analytics

San Francisco, California, United States

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Not SpecifiedCompensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Sports Analytics, Betting, Fantasy Sports, Data ProductsIndustries

Requirements

Candidates must possess a BS/BA degree in Mathematics, Computer Science, or a related STEM field, with a minimum of 5 years of demonstrated experience writing production-level code in Python and SQL, preferably MySQL. Experience with Airflow, Kubernetes, building end-to-end ETL pipelines, utilizing REST APIs, and version control (git) is required. Proficiency in continuous integration and deployment, shell scripting, cloud computing infrastructures (AWS), web scraping, cleaning unstructured data, and knowledge of data science/machine learning concepts are also necessary. A strong understanding of sports betting, specifically the NBA or NFL, is essential to inform work with complex datasets.

Responsibilities

The Data Engineer will be responsible for architecting low-latency, real-time analytics systems, including raw data collection, feature development, and endpoint production. They will build new sports betting data products and predictions offerings, integrating large and complex real-time datasets into new consumer and enterprise products. The role involves developing production-level predictive analytics into enterprise-grade APIs, supporting production systems, and triaging issues during live sporting events. Additionally, the Data Engineer will contribute to the design and implementation of new, fully-automated sports data delivery frameworks.

Skills

Python

SQL

MySQL

Airflow

Kubernetes

Data Engineering

Predictive Analytics

API Development

Real-time Data Processing

Low-latency Systems

Swish Analytics

Sports analytics and optimization tools provider

About Swish Analytics

Swish Analytics specializes in sports analytics and optimization tools for daily fantasy sports and sports betting, focusing on major U.S. leagues like the NFL, MLB, NBA, and NHL. The company uses an advanced machine learning system to analyze large datasets, providing accurate sports predictions and optimized lineups. This helps users, including individual bettors and professional operators, make informed decisions about their bets and fantasy picks. Swish Analytics differentiates itself by being an Authorized MLB Data Distributor, establishing trust in the sports betting community. Operating on a subscription-based model, users can access various levels of tools and analytics, starting with a free trial. The goal of Swish Analytics is to maximize return on investment for clients by identifying the best bets and balancing risk and reward for long-term success.

San Francisco, CaliforniaHeadquarters

2014Year Founded

$6.5MTotal Funding

EARLY_VCCompany Stage

Fintech, AI & Machine Learning, Financial ServicesIndustries

51-200Employees

Benefits

Remote Work Options

Risks

Increased competition from AI-driven startups could erode Swish Analytics' market share.

Consumer privacy concerns may impact Swish Analytics' data collection practices.

Potential regulation of sports betting advertising could affect Swish Analytics' revenue streams.

Differentiation

Swish Analytics uses proprietary algorithms for accurate sports predictions and optimized lineups.

The company is an Authorized MLB Data Distributor, enhancing its credibility in sports betting.

Swish Analytics offers a subscription model with free trials, attracting diverse user segments.

Upsides

Increased legalization of sports betting in the U.S. expands Swish Analytics' market opportunities.

The rise of AI-driven personalized betting experiences aligns with Swish Analytics' machine learning expertise.

Growing interest in micro-betting offers Swish Analytics a chance to expand its offerings.

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