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 empowers the PrizePicks business across all departments to make data-driven decisions by providing reliable and insightful data. As an Analytics Engineer, you will be a key contributor to designing, building, and maintaining the data models and transformation pipelines that underpin our analytical capabilities. This role requires a strong focus on data quality, consistency, and the delivery of actionable insights.

What you’ll do:

• Partner with Data Engineering to build and optimize robust data pipelines that ensure data accessibility and reliability. Collaborate with Business Intelligence to implement crucial business logic that powers BI dashboards, directly impacting key business decisions and providing broad organizational exposure.
• Experience in designing and implementing data transformation workflows using best practices in data modeling, ELT processes, and ensuring data quality and consistency for analytical use cases.
• Design and implement complex data transformation logic primarily in SQL with a focus on creating reusable data models that support various analytical needs. Build and maintain dbt models to ensure data accuracy, consistency, and reliability.
• Collaborate with data engineers, data analysts, and business stakeholders to understand data requirements, define key metrics, and deliver actionable insights through data models and reporting solutions.
• Implement and maintain data quality checks and validation processes to ensure the accuracy and reliability of data used for analysis. Develop comprehensive documentation of data models, and transformation logic to facilitate data understanding.
• Develop and manage CI/CD pipelines to automate and streamline the deployment of data solutions. Ensure that data workflows are thoroughly tested, integrated, and deployed efficiently, following best practices for version control, automation, and quality assurance.
• Experience in championing and implementing data governance principles, such as data lineage and data cataloging, to improve data discoverability, usability, and trust for analytical purposes.
• Serve as a leading voice within the Analytics Engineering team by staying current with emerging analytical techniques, data modeling best practices, and analytics engineering trends. Actively mentor other team members and promote a data-driven culture by leading through example and sharing best practices.
• On-call rotation support, the on-call is shared across Analytics and Data Engineering teams.

What you have:

• 5+ years of experience in an Analytics Engineering, Data Engineering, or data-oriented software engineering role creating and pushing end-to-end data engineering pipelines.
• 1+ years of experience acting as technical lead and providing mentorship and feedback to junior engineers.
• Graduate degree in a quantitative field: Computer Science, Mathematics, Statistics, Business Analytics, Engineering) or equivalent experience.
• Proven experience in guiding complex projects and mentoring other engineers.
• Experience building and optimizing data pipelines for analytics, with a focus on data transformation and modeling.
• Experience in integrating data from various sources to support analytical needs, including familiarity with data warehousing principles and ELT processes.
• Experience in most of the following:
  • ELT tools: dbt
  • SQL/NoSQL databases/warehouses: Postgres, BigQuery, BigTable, etc
  • Replication Services: Data Stream, Hevo, Kafka, etc.
  • Scripting languages: SQL, Python.
  • Familiarity with cloud platform data services, such as data warehousing solutions (e.g., BigQuery, Snowflake, Redshift) and storage (e.g., Cloud Storage, S3).
  • Code version control: Git
  • Data pipeline and workflow tools relevant to analytics: Dataform, Spark, orchestration tools used in data analytics workflows (e.g., Argo Workflows, Airflow, cloud-based workflow services), and customer dat