About the job
Who are we?
Smarkets: Shaping the Future of Betting
At Smarkets, we operate one of the most advanced prediction markets globally, with a staggering £29 billion in volume processed since our inception in 2010. Our platform engages over 200,000 traders worldwide, revolutionizing betting across various sectors, including sports and political markets, by providing the most competitive prices and fairest odds.
Our tech stack is engineered for scalability, reliability, and performance, utilizing Linux, Kafka, Postgres, and Kubernetes, while Python 3, C++, Rust, and React underpin our platform. We construct infrastructure that institutions can rely on while ensuring trading remains accessible to all users. Our resilience is evident as we have thrived through every market trend and competitive landscape.
What sets us apart is our exceptional team. We foster a high-performance culture where talent flourishes, merging extensive business knowledge with a strategic approach to drive growth.
If you're eager to help redefine the future of prediction markets with innovative technology and a customer-centric approach, Smarkets is your ideal workplace.
The Team
Our Data Team plays a crucial role in harnessing the vast amount of data generated at Smarkets to derive insights that propel our business forward. Given the extensive range of data we produce, from sports event metrics to payment information and user analytics, there are abundant opportunities for the team to create significant business impact.
Currently, our team's responsibilities encompass three primary domains:
Data Engineering: Developing and maintaining ETL pipelines, APIs, and data infrastructure such as Redshift or BigQuery;
Data Science and Machine Learning: Exploring data, training ML models, and implementing ML Ops to uncover fresh insights;
Analytics and Reporting: Crafting data models and dashboards while automating reporting pipelines for various teams, stakeholders, and third parties.
A typical week for a data engineer within our Data Team might involve:
Creating a new Python ETL pipeline to segment users based on their sports interests by analyzing behavior, optimizing marketing communications for these users;
Developing a new endpoint for a Flask API, implementing unit tests, and deploying the updated version into our production Kubernetes cluster;
Training and assessing an ML model to identify specific user patterns, contributing to our data-driven decision-making.
