Data Platform Engineer

PrizePicks is the fastest-growing sports company in North America, recognized for its leading platform in Daily Fantasy Sports. As a Data Platform Engineer, you will build and maintain a scalable data platform, integrating low-latency ML models to enhance key metrics in the sports betting and fantasy ecosystems.

Responsibilities:

• 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 oddsmaking 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%

Requirements:

• 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/PubSub) and building low-latency services to serve stream ingestion and processing, which will serve model inference
• Proficient with Containerization, Docker, Kubernetes and cluster-level management
• Deep experience building a platform for managing the full Data lifecycle, including setting up a data exploration environment
• Expert in coding with Python and Go
• Deep experience with Cloud services, preferred with GCP services (BigQuery, Cloud Functions, GKE) or AWS equivalents
• Excellent communication skills, stakeholder management and outstanding problem-solving skills
• Extensive experience in Big data technologies like Spark, Flink, Kafka or Kinesis, Argo/Airflow, Polaris, OpenMetadata, Iceberg, Lakehouse, Redis, Elasticsearch, and Databases
• Experience with building REST APIs, package management and have built libraries
• Should have been a key contributor to projects through the entire development lifecycle from concept to release
• Experience implementing infrastructure while enforcing best practices for deployment of a large scale data platform
• Background in Daily Fantasy Sports (DFS), oddsmaking, or high-frequency trading
• Experience building and scaling data platforms that successfully bridge batch historical data with real-time event streams
• Enabling self-service for Data teams for pipeline development and deployment
• Enabling AI agents for repetitive tasks and AI coding for faster and iterative software development