Data Platform Engineer

Responsibilities

• Design and build a scalable data platform for batch and streaming use cases.
• Build and maintain data catalog and data lineage capabilities.
• Design and enforce robust data security architectures and controls.
• Build platforms for deploying low-latency services supporting streaming and near-real-time use cases.
• Support real-time decisions including dynamic oddsmaking, risk analysis, and deposit defaults.
• Establish best practices for model deployment, monitoring, and CI/CD for data pipeline deployment.
• Enable complete observability for batch and streaming data platforms and maintain 99.99% availability.

Requirements

• 3+ years of experience in platform engineering, including deploying and maintaining scalable data platforms in high-traffic production environments.
• Proficiency with streaming architectures such as Kafka, Flink, and Pub/Sub, and with building low-latency services for stream ingestion and processing.
• Proficiency with containerization, Docker, Kubernetes, and cluster-level management.
• Deep experience managing the full data lifecycle, including data exploration environments.
• Expertise in Python and Go.
• Deep experience with cloud services, preferably Google Cloud services such as BigQuery, Cloud Functions, and Google Kubernetes Engine, or AWS equivalents.
• Strong communication, stakeholder-management, and problem-solving skills.
• Experience with big data and data-platform technologies including Spark, Flink, Kafka or Kinesis, Argo, Airflow, Polaris, OpenMetadata, Iceberg, Lakehouse architectures, Redis, Elasticsearch, and databases.
• Experience building REST APIs, managing packages, and developing libraries.
• Experience contributing to projects through the complete development lifecycle, from concept through release.

Nice-to-Haves

• Experience implementing infrastructure and enforcing deployment best practices for large-scale data platforms.
• Background in daily fantasy sports, oddsmaking, or high-frequency trading.
• Experience bridging batch historical data with real-time event streams.
• Experience enabling self-service pipeline development and deployment for data teams.
• Experience enabling AI agents for repetitive tasks and AI-assisted coding.

Compensation and Benefits

• Typical salary range: $145,000–$175,000.
• Company-subsidized medical, dental, and vision plans.
• 401(k) plan with company match.
• Annual bonus.
• Flexible paid time off.
• Paid leave programs, including 16-week paid parental leave and disability benefits.
• Workplace flexibility and modern work schedules.
• Company-wide in-person events and team outings.
• Lifestyle enhancement program.
• Company equipment with Windows and Mac options.
• Annual performance reviews with career-development opportunities.