Big Data Spark Administrator

Job Description

Job Title : Big Data Spark Administrator

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Location : Berkley Heights, NJ (Onsite)- 5 days a week at client office

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Hire Type : Long Term Contract

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Job Description

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• 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

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• 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

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