The Streaming Data Platform team is responsible for building, managing complex stream processing topologies using the latest open-source tech stack, build metrics and visualizations on the generated streams and create varied data sets for different forms of consumption and access patterns. We're looking for a seasoned Staff Software engineer to help us build and scale the next generation of streaming platforms and infrastructure at Fanatics Commerce. 

Responsibilities  

1. Design and build real-time streaming data platforms that enable sub-second data availability to MPP databases (StarRocks, Apache Pinot, Apache Druid)
2. Architect and implement data pipelines that handle complex data skews and leverage data colocation strategies for optimal query performance
3. Fine-tune Apache Iceberg table parameters including compaction policies, partition evolution, file sizing, and snapshot management for streaming workloads
4. Provide technical leadership on streaming architectures, guiding teams on optimal patterns for real-time data ingestion, processing, and materialization into MPP systems
5. Contribute to open-source MPP database projects (StarRocks, Apache Pinot, Apache Druid) with performance improvements, bug fixes, and feature enhancements
6. Design data distribution strategies and bucketing schemes to minimize shuffle operations and maximize colocation benefits in distributed queries
7. Optimize existing streaming infrastructure through profiling, identifying bottlenecks in data skew handling, and implementing dynamic rebalancing strategies 

 
Qualifications  

1. 9+ years of software development experience
2. Proven experience building production-grade streaming pipelines to MPP databases (StarRocks/Pinot/Druid) with consistent sub-second latency
3. Strong understanding of data skew patterns and mitigation techniques including salting, bucketing, adaptive partitioning, and custom key distribution
4. Hands-on experience with data colocation strategies in distributed systems to optimize for local joins and reduce network shuffles
5. Expert-level knowledge of Apache Iceberg for streaming workloads: snapshot isolation, file format tuning, compaction strategies, partition evolution, and metadata management
6. Demonstrated open-source contributions to MPP databases or adjacent projects (commits, PRs, design proposals, community engagement)
7. Proficiency in Java and/or C++
8. Proficiency in Kafka, Flink and Spark
9. Deep expertise in SQL optimization, distributed query planning, and physical execution plans in MPP systems
10. Experience with optimizations like: tablet distribution, bucketing, colocation groups, materialized views, and primary key models