Senior Data Engineer, Fanatics

• 5+ years of professional data engineering or software engineering experience
• Production experience designing, building, and operating Kafka-based streaming systems (required)
• Hands-on experience with Apache Flink for building real-time streaming applications in production
• Experience with Snowflake, including Snowpipe Streaming, dynamic tables, streams, and tasks for near real-time data ingestion and transformation
• Experience building semantic layers or analytical cubes using Cube.dev and/or MicroStrategy
• Solid understanding of data skew challenges and mitigation techniques (bucketing, salting, repartitioning, adaptive strategies)
• Working knowledge of Apache Iceberg for streaming or near-real-time workloads, including table layout and compaction concepts
• Proficiency in Java and/or Python
• Strong SQL skills and understanding of distributed query execution and performance tuning
• Experience operating data pipelines in production, including monitoring, alerting, and incident response

Desirable

• As a Senior Data Engineer, you will design, build, and operate production-grade streaming data pipelines and storage systems that support near real-time analytics across Fanatics Commerce
• You will collaborate closely with platform and analytics teams, drive architectural decisions, and help shape the future of our data infrastructure
• Design, build, and operate real-time streaming data pipelines using Apache Flink and Kafka, delivering low-latency data to analytical and operational consumers
• Build and maintain Snowpipe Streaming integrations to continuously ingest event data into Snowflake with minimal latency
• Develop and optimize Snowflake data models, including dynamic tables, streams, and tasks to support near real-time analytics workloads
• Design and maintain semantic layers and BI cubes using Cube.dev and/or MicroStrategy to support self-serve analytics and reporting
• Implement and maintain data pipelines that handle moderate to high data skew using techniques like bucketing, salting, and adaptive partitioning
• Work with Apache Iceberg tables for streaming workloads, including partitioning strategies, compaction tuning, file sizing, and snapshot management
• Collaborate with platform and analytics teams to model data for different consumption patterns, including real-time dashboards and analytical queries
• Optimize streaming jobs and storage layouts to improve query performance, reduce latency, and control infrastructure cost
• Diagnose and resolve performance bottlenecks related to ingestion, skew, and distributed query execution
• Contribute code, reviews, and documentation to shared data platform components and internal frameworks
• Participate in design discussions and provide technical input on streaming architecture and data modeling decisions