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PrizePicks

Data Platform Engineer

Atlanta, GA preferred, Remote (Remote)$145k–$175kmidAdded today

About this role

PrizePicks seeks a Data Platform Engineer to build and scale a modern data platform supporting batch, streaming, and real-time ML inference for a fast-growing sports betting and daily fantasy platform. You'll design low-latency architectures, implement data governance, and ensure 99.99% availability across production systems.

What you'll do

  • Design and build scalable batch and streaming data platforms with data catalog and lineage capabilities
  • Develop low-latency real-time services for deploying ML models and streaming data pipelines in sub-100ms timeframes
  • Implement data security architectures, controls, and governance frameworks across the platform
  • Champion CI/CD best practices, monitoring, and observability for data pipeline and model deployments
  • Manage platform operations to maintain 99.99% availability and enable model deployment workflows
  • Build and maintain data exploration environments and manage the full data lifecycle

What they're looking for

  • Platform engineering with 3+ years in production data systems
  • Streaming architectures (Kafka, Flink, Pub/Sub)
  • Docker, Kubernetes, and cluster management
  • Python and Go programming
  • GCP (BigQuery, Cloud Functions, GKE) or AWS cloud services
  • Big data technologies (Spark, Flink, Airflow, Iceberg, Lakehouse)
  • REST API development and library design
  • Data governance tools (OpenMetadata, Polaris)
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PrizePicks

PrizePicks operates a daily fantasy sports platform with a mobile-first experience. They are hiring Software Engineers (across levels II-III) skilled in TypeScript, React Native, and Ruby on Rails, as well as Data Engineers to build infrastructure supporting analytics and platform growth.

Website
prizepicks.com
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Likely interview questions

  • Describe your experience building and deploying scalable data platforms in high-traffic production environments—what were the key challenges?
  • How have you designed streaming architectures to achieve sub-100ms latency for real-time inference, and what trade-offs did you consider?
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