Data Engineer

PrizePicks is the fastest-growing sports company in North America and is seeking a Data Engineer to join their Data Engineering team. The role involves building the data infrastructure that powers analytics, machine learning, and business decision-making, while working on well-defined pipeline and data tasks under guidance.

Responsibilities:

• Develop and execute well-defined data engineering tasks — creating and modifying data models, writing ingestion scripts, and updating transformations following engineering standards
• Orchestrate data flow through PrizePicks' medallion lakehouse architecture: Landing-Bronze-Silver-Gold
• Deploy, execute, and monitor data jobs and workflows, respond to failures following established runbooks during oncall
• Author robust Python and SQL code following team conventions, with guidance on style, structure, and testing
• Adhere to team standards for model organization, naming, and documentation
• Conduct code reviews - give and receive feedback constructively
• Implement unit tests and adopt what good test coverage looks like for data pipelines
• Own assigned pipeline tasks - follow through, communicate blockers early, and see work to completion
• Collaborate actively in agile rituals and team discussions
• Document technical builds: models, runbooks, and decisions. Share learnings with the team
• Partner with Analytics, MLE, and Product stakeholders to understand how the data you produce gets used and find opportunities to better serve stakeholders

Requirements:

• A bachelor's degree in Computer Science, Mathematics, or a related quantitative field, or equivalent hands-on experience
• Good knowledge of Python - you can write a script, work with data structures, and follow a coding style guide
• Experience working with cloud environments (GCP, AWS, Azure)
• Familiarity with SQL - you can write queries, understand joins, and read a schema
• Familiarity with data engineering concepts: ETL/ELT, APIs, data pipelines, or similar
• Curiosity about how data moves through systems and a desire to understand the full stack
• Clear written and verbal communication skills - you can explain what you're working on and flag when you're stuck
• Ownership mindset: takes responsibility for assigned tasks and follows through reliably
• Exposure to GCP
• Familiarity with a workflow orchestration tool (Airflow, Argo, etc.)
• Experience with Big Query, dbt, Spark, Kafka, or Iceberg
• Experience with containerization or Kubernetes
• Experience with Infrastructure as Code tooling (Terraform, CrossPlane)