Apache Spark / Big Data Administrator (Berkeley Heights)
Mphasis • Berkeley Heights, NJ, US
1 day ago
Job type
- Full-time
Job description
Role: Apache Spark / Big Data Administrator
Location: Berkley Heights, NJ
Work Type: 5 days a week at client office
Job Description
- 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.
- Focuses on cluster health, performance tuning, and production data pipelines.
- Technical Experience: Deep hands-on experience with Apache Spark architecture (Driver, Workers, Executors, RDDs/DataFrames) and cluster managers (YARN, Kubernetes, or Mesos).
- OS & Scripting: Strong proficiency in Linux/Unix administration and scripting languages (Bash, Python, or Scala).
- Ecosystem Knowledge: Familiarity with big data and streaming tools like Hadoop HDFS, Kafka, Hive, or monitoring suites (Splunk, Prometheus, Grafana).
- Debugging Skills: Strong analytical capabilities to inspect logs, thread dumps, and metrics under heavy production loads
- Excellent coordination with stakeholders and communication skills
Must have experience with below responsibilities
- Cluster Management: Install, configure, and maintain Apache Spark clusters and node infrastructure in production and staging environments.
- 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.
- Troubleshooting: Diagnose and resolve out-of-memory errors, executor failures, shuffle issues, and node crashes in distributed workloads.
- Pipeline & Integration Support: Support data engineers and developers in managing Spark applications, streaming jobs, and integrations with message brokers like Kafka.
- Security & Access Control: Implement security principles, role-based access control, authentication, and data encryption across the cluster.
- Backup & Disaster Recovery: Execute backup, recovery, and failover plans for Spark applications, configurations, and critical data stores.
Patching & Upgrades: Apply software patches, driver updates, and version upgrades with minimal disruption to ongoing production jobs