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Product Support Engineer "Hadoop SME"

Acceldata is a data observability platform provider specializing in enterprise-scale data systems optimization.
Data
Mid-Level Software Engineer
In-Person
3+ years of experience
Enterprise SaaS

Job Description

Acceldata is seeking an Apache Hadoop Subject Matter Expert (SME) to join their team as a Product Support Engineer. This role focuses on designing, optimizing, and scaling Spark-based data processing systems, requiring deep expertise in Spark architecture and core functionalities. The position involves building resilient, high-performance distributed data systems and collaborating with engineering teams to deliver high-throughput Spark applications. The role demands expertise in real-time processing, big data analytics, and streaming solutions. Working in a 24x7 environment with rotational shifts, the ideal candidate will have comprehensive knowledge of the big data ecosystem, including Hadoop, Hive, Kafka, and cloud platforms. This position offers an opportunity to work with cutting-edge data solutions in a fast-paced, dynamic environment, making a significant impact on large-scale data processing systems.

Last updated 20 days ago

Responsibilities For Product Support Engineer "Hadoop SME"

  • Design and optimize distributed Spark-based applications for low-latency, high-throughput performance
  • Provide expert-level troubleshooting for data and performance issues related to Spark jobs and clusters
  • Work with large-scale data pipelines using Spark's core components
  • Conduct performance analysis, debugging, and optimization of Spark jobs
  • Collaborate with DevOps teams to manage Spark clusters
  • Design and implement real-time data processing solutions
  • Work in rotational shifts in a 24x7 environment

Requirements For Product Support Engineer "Hadoop SME"

Python
Kubernetes
  • Expert knowledge of Spark architecture, execution models, and components
  • Solid understanding of ETL pipelines, data partitioning, shuffling, and serialization techniques
  • Knowledge of big data technologies (Hadoop, Hive, Kafka, HDFS, YARN)
  • Demonstrated ability to tune Spark jobs and troubleshoot performance bottlenecks
  • Hands-on experience in running Spark clusters on cloud platforms (AWS, Azure, GCP)
  • Experience with containerized Spark environments using Docker and Kubernetes