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 450 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? 

The Analytics Team is responsible for building and maintaining analytics tools and workflows to support the PrizePicks business across all departments. At the core of these operations is data. As a Staff Data Engineer, you will develop, maintain, test, and lead data infrastructure projects and help PrizePicks offer a scalable, reliable, and smarter data ecosystem for faster data-driven decisions.

What you’ll do:

• Enhance the capabilities of our existing Core Data Platform and develop new integrations with both internal and external APIs within the Data organization.
• Experienced in building robust real-time and batch data pipelines with a focus on data modeling, ETL/ELT best practices, and reliability. Adept at defining and driving data architecture standards to ensure scalable, governed solutions across teams.
• Develop and maintain advanced data pipelines and transformation logic using Python and Go, ensuring efficient and reliable data processing.
• Collaborate with Data Scientists and Data Science Engineers to support the needs of advanced ML development.
• Collaborate with Analytics Engineers to enhance data transformation processes, streamline CI/CD pipelines, and optimize team collaboration workflows Using DBT.
• Work closely with DevOps and Infrastructure teams to ensure the maturity and success of the Core Data platform.
• Guide teams in implementing and maintaining comprehensive monitoring, alerting, and documentation practices, and coordinate with Engineering teams to ensure continuous feature availability.
• Design and implement Infrastructure as Code (IaC) solutions to automate and streamline data infrastructure deployment, ensuring scalable, consistent configurations aligned with data engineering best practices.
• Build and maintain CI/CD pipelines to automate the deployment of data solutions, ensuring robust testing, seamless integration, and adherence to best practices in version control, automation, and quality assurance.
• Experienced in designing and automating data governance workflows and tool integrations across complex environments, ensuring data integrity and protection throughout the data lifecycle.
• Serve as a Staff Engineer within the broader PrizePicks technology organization by staying current with emerging technologies, implementing innovative solutions, and sharing knowledge and best practices with junior team members and collaborators.
• Ensure code is thoroughly tested, effectively integrated, and efficiently deployed, in alignment with industry best practices for version control, automation, and quality assurance.
• Mentor and support junior engineers by providing guidance, coaching and educational opportunities
• Provide on-call support as part of a shared rotation between the Data and Analytics Engineering teams to maintain system reliability and respond to critical issues.

What you have:

• 7+ years of experience in a data Engineering, or data-oriented software engineering role creating and pushing end-to-end data engineering pipelines.
• 3+ years of experience acting as technical lead and providing mentorship and feedback to junior engineers.
• Extensive experience building and optimizing cloud-based data streaming pipelines and infrastructure.
• Extensive experience exposing real-time predictive model outputs to production-grade systems leveraging large-scale distributed data processing and model training.
• Experience in most of the following:
  • SQL/NoSQL databases/warehouses: Postgres, BigQuery, BigTable, Materialize, AlloyDB, etc
  • Replication/ELT services: Data Stream, Hevo, etc.
  • Data Transformation services: Spark, Dataproc, etc
  • Scripting languages: SQL, Python, Go.
  • Cloud platform services in GCP and analogous systems: Cloud Storage, Cloud Compute Engine, Cloud Functions, Kubernetes Engine etc.
  • Data Processing and Messaging Systems: Kafka, Pulsar, Fli