Data Engineer
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About the Role
At PrizePicks, 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?
We are looking for a Data Engineer 1 to join our Data Engineering team and help build the data infrastructure that powers analytics, machine learning, and business decision-making at PrizePicks. This is an entry-level role where you will work on well-defined pipeline and data tasks under guidance, building your skills across the full data engineering stack while contributing to a modern lakehouse platform.
What you’ll do:
Pipeline & Data Tasks
- 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
Code Quality & Development
- 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
Collaboration & Ownership
- 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.
What you have:
- 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
What makes you stand out:
- 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)
Where you’ll live:
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