Data Engineer (Spain) at Swish Analytics

Barcelona, Catalonia, Spain

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Not SpecifiedCompensation

Junior (1 to 2 years), Mid-level (3 to 4 years)Experience Level

Full TimeJob Type

UnknownVisa

Sports Analytics, Betting, Fantasy SportsIndustries

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
Demonstrated experience with Kubernetes
Experience building end-to-end ETL pipelines
Experience utilizing REST APIs
Experience with version control (git), continuous integration and deployment, shell scripting, and cloud-computing infrastructures (AWS)
Experience with web scraping and cleaning unstructured data
Knowledge of data science and machine learning concepts
A strong interest for sports and sports betting, with an emphasis on Tennis
An understanding of US-based sports including the NFL, NBA, MLB, NHL, College Football, College Basketball, and the ability to use your knowledge of the sport to inform your work with complex datasets

Responsibilities

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

Skills

Python

SQL

MySQL

Airflow

real-time data

data pipelines

APIs

analytics

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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