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Urgent! Fanatics - Staff Software Engineer - Big Data Platform Job Opening In Hyderabad – Now Hiring Fanatics

Fanatics Staff Software Engineer Big Data Platform



Job description

<p><p><b>Job Description :</b><br/><br/>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.<br/><br/>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.<br/><br/><b>Responsibilities :</b><br/><br/>- Build data platforms and streaming engines that are real-time and batch in nature.<br/><br/>- Optimize existing data platforms and infrastructure while exploring other technologies.<br/><br/>- Provide technical leadership to the data engineering team on how data should be stored and processed more efficiently and quickly at scale.<br/><br/>- Build and scale stream & batch processing platforms using the latest open-source technologies.<br/><br/>- Work with data engineering teams and help with reference implementation for different use cases.<br/><br/>- Improve existing tools to deliver value to the users of the platform.<br/><br/>- Work with data engineers to create services that can ingest and supply data to and from external sources and ensure data quality and timeliness.<br/><br/><b>Qualifications :</b><br/><br/>- 8+ years of software development experience with at least 3+ years of experience on open-source big data technologies.<br/><br/>- Knowledge of common design patterns used in Complex Event Processing.<br/><br/>- Knowledge in Streaming technologies : Apache Kafka, Kafka Streams, KSQL, Spark, Spark Streaming.<br/><br/>- Proficiency in Java, Scala.<br/><br/>- Strong hands-on experience in SQL, Hive, Spark SQL, Data Modeling, Schema design.<br/><br/>- Experience and deep understanding of traditional, NoSQL and columnar databases.<br/><br/>- Experience of building scalable infrastructure to support stream, batch and micro-batch data processing.<br/><br/>- Experience utilizing Apache Iceberg as the backbone of a modern lakehouse architecture, supporting schema evolution, partitioning, and scalable data compaction across petabyte-scale datasets.<br/><br/>- 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.<br/><br/>- 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.</p><br/></p> (ref:hirist.tech)


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