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.