Data Platform Engineer

At Prize Picks, we are the fastest-growing sports company in North America, as recognized by Inc. 5000. As the leading platform for Daily Fantasy Sports, we cover a diverse range of sports leagues, including the NFL, NBA, and Esports titles like League of Legends and Counter-Strike. Our team of over 550 employees thrives in an inclusive culture that values individuals from diverse backgrounds, regardless of their level of sports fandom.

Ready to reimagine the DFS industry together?

As a Data Platform Engineer, you will contribute to building the modern Data platform at Prizepicks to scale and product ionize our core data engineering, data analytics and machine learning capabilities. Your work will directly impact key metrics like Time-to-Bet, Deposit Velocity, and Platform Integrity by integrating robust, low-latency ML models across our sports betting and daily fantasy ecosystems.
What you'll do:

• Build Scalable Data Platform: Design and build the Data platform for Batch and Streaming use cases. You will build and maintain a platform with cutting-edge technologies and enable data users by building data catalog and data lineage capabilities. You will be contributing to design and enforce robust data security architectures and controls.
• Real-Time data platform at Scale: Build platform for deploying low-latency services to pipe data for streaming or near real time use cases. You will power real-time decisions across the platform, from dynamic odds making and risk analysis to smart deposit defaults.
• Data Platform Ops: You will champion best practices for model deployment, monitoring, and CI/CD for Data pipeline deployment. You will enable complete observability for batch and streaming data platform and ensure the availability of 99.99%

What you have:

• 3+ years of experience in Platform Engineering, with a proven track record of deploying and maintaining scalable Data platforms in high-traffic production environments.
• Proficient in streaming architectures (Kafka/Flink/Pub Sub) and building low-latency services to serve stream ingestion and processing, which will serve model inference in