Staff Software Engineer

Job Description

Job Description 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 Build data platforms and streaming engines that are real-time and batch in nature Optimize existing data platforms and infrastructure while exploring other technologies Provide technical leadership to the data engineering team on how data should be stored and processed more efficiently and quickly at scale Build and scale stream & batch processing platforms using the latest open-source technologies Work with data engineering teams and help with reference implementation for different use cases Improve existing tools to deliver value to the users of the platform Work with data engineers to create services that can ingest and supply data to and from external sources and ensure data quality and timeliness Qualifications 8+ years of software development experience with at least 3+ years of experience on open-source big data technologies Knowledge of common design patterns used in Complex Event Processing Knowledge in Streaming technologies: Apache Kafka, Kafka Streams, KSQL, Spark, Spark Streaming Proficiency in Java, Scala Strong hands-on experience in SQL, Hive, Spark SQL, Data Modeling, Schema design. Experience and deep understanding of traditional, NoSQL and columnar databases Experience of building scalable infrastructure to support stream, batch and micro-batch data processing Experience utilizing Apache Iceberg as the backbone of a modern lakehouse architecture, supporting schema evolution, partitioning, and scalable data compaction across petabyte-scale datasets. Experience utilizing AWS Glue as a centralized data catalog to register and manage Iceberg tables, enabling seamless integration with real-time query engines and improving data discovery across distributed systems. Experience working with Druid/ StarRocks /Apache Pinot etc. , powering low-latency queries, routine Kafka ingestion, and fast joins across both historical and real-time data.