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# Apache Spark / Big Data Administrator

- **Company:** Envision, Inc
- **Location:** Berkeley Heights, NJ, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** User Authentication, Bash Shell, Big Data, System Configuration, Data Stores, Software Debugging, Apache Hadoop, Hadoop Distributed File System, Apache Hive, Python (Programming Language), Message Broker, Node.Js, Operational Databases, Performance Tuning, Role-Based Access Control, Prometheus, Mesos, Service Pack, Data Streaming, Systems Integration, Backup and Restore, Scripting, Cloud Platform System, Apache Yarn, System Availability, Grafana, Apache Spark, Kubernetes, Apache Kafka, Splunk
- **Published:** October 15, 2026
- **Apply:** https://www.thejobnetwork.com/job/7c6c1eb6-d971-4aef-82aa-76759c6e19ba/apache-spark-big-data-administrator

## About the Role

* 10+ years solid experience with deploying, configuring, monitoring, and troubleshooting Apache Spark clusters and associated big data frameworks (such as Hadoop, Kafka, or cloud-based data platforms) to ensure high availability and performance.
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 * Focuses on cluster health, performance tuning, and production data pipelines.
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 * Technical Experience: Deep hands-on experience with Apache Spark architecture (Driver, Workers, Executors, RDDs/DataFrames) and cluster managers (YARN, Kubernetes, or Mesos).
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 * OS & Scripting: Strong proficiency in Linux/Unix administration and scripting languages (Bash, Python, or Scala).
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 * Ecosystem Knowledge: Familiarity with big data and streaming tools like Hadoop HDFS, Kafka, Hive, or monitoring suites (Splunk, Prometheus, Grafana).
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 * Debugging Skills: Strong analytical capabilities to inspect logs, thread dumps, and metrics under heavy production loads
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 * Excellent coordination with stakeholders and communication skills
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Must have experience with below responsibilities

## Description

* Cluster Management: Install, configure, and maintain Apache Spark clusters and node infrastructure in production and staging environments.
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 * Performance & Monitoring: Monitor cluster health, resource utilization (CPU, memory), and execution logs using tools like Spark UI, Splunk, or Elastic. Identify bottlenecks and optimize job execution times.
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 * Troubleshooting: Diagnose and resolve out-of-memory errors, executor failures, shuffle issues, and node crashes in distributed workloads.
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 * Pipeline & Integration Support: Support data engineers and developers in managing Spark applications, streaming jobs, and integrations with message brokers like Kafka.
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 * Security & Access Control: Implement security principles, role-based access control, authentication, and data encryption across the cluster.
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 * Backup & Disaster Recovery: Execute backup, recovery, and failover plans for Spark applications, configurations, and critical data stores.
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Patching & Upgrades: Apply software patches, driver updates, and version upgrades with minimal disruption to ongoing production jobs