Data Engineering Manager

PrizePicks is the fastest-growing sports company in North America, recognized for its leading platform in Daily Fantasy Sports. The Data Engineering Manager will lead a team responsible for building and operating scalable data engineering pipelines, ensuring data is trusted and accessible to support analytics and business decision-making.

Responsibilities:

• Lead, mentor, and develop a team of Data Engineers through regular 1:1s, coaching, and performance feedback
• Own hiring, onboarding, career development, and performance management for the team
• Foster a culture of accountability, ownership, collaboration, and continuous improvement
• Balance delivery expectations with sustainable team practices
• Own the design, development, and operation of data ingestion, transformation, and storage pipelines (batch and streaming)
• Ensure reliability, scalability, performance, and cost efficiency of data systems
• Drive best practices in data modeling, data quality, observability, and operational excellence
• Review and guide technical designs, architecture decisions, and code quality
• Partner with platform and infrastructure teams on tooling, security, and scalability
• Translate business and analytics requirements into scalable technical solutions
• Partner with Product, Analytics Engineering, Data Science, and Business teams to deliver trusted datasets and metrics
• Own sprint planning, prioritization, and delivery commitments
• Communicate progress, risks, and tradeoffs clearly to stakeholders and leadership
• Establish and enforce data engineering standards, patterns, and documentation
• Support data governance, data contracts, and schema management
• Ensure compliance with security, privacy, and data retention requirements

Requirements:

• A graduate degree in Computer Science, Mathematics, or a related quantitative field
• 8+ years of experience in Data Engineering or related roles
• 3+ years leading engineers (hiring, coaching, delivery accountability)
• Strong distributed systems fundamentals (scaling, reliability, data correctness)
• Hands-on ability in Python and/or Java (can dive into code and unblock delivery)
• Proficiency with SQL and at least one programming language (Python, Java, or Scala)
• Experience with orchestration tools (Airflow, Argo, etc.)
• Solid understanding of data modeling, ETL/ELT patterns, and data reliability
• Experience with streaming technologies (Kafka, Flink, Spark Streaming)
• Experience with modern data warehouses / lakehouses (BigQuery, Snowflake, Redshift, Iceberg)
• Familiarity with analytics engineering tools (dbt, semantic layers)
• Exposure to data governance, observability, and data quality tooling
• Self-starter + ownership: drives ambiguous problems to durable solutions
• Builds and grows teams: mentors, raises the bar, and develops talent
• Experience with cloud data ecosystems (GCP - Preferred, AWS, or Azure)