Location: Charlotte, NC
Salary: $69.00 USD Hourly - $74.00 USD Hourly
Description: Hadoop / HPE MapR EngineerLocation: Charlotte, NC (On-site/Hybrid options based on company policy)
About the RoleWe are looking for an experienced
Hadoop / HPE MapR Engineer to design, build, operate, and optimize large-scale distributed data platforms. In this role, you will support and evolve our
HPE MapR-based Hadoop ecosystem, ensuring high availability, strong performance, security, and reliability across enterprise data workloads.
You will work closely with engineering, data, and infrastructure teams to maintain production-grade clusters and drive improvements in observability, scalability, and automation.
Responsibilities- Design, deploy, and maintain large-scale HPE MapR Hadoop clusters in production environments.
- Ensure high availability, performance optimization, reliability, and security across the platform.
- Manage, configure, and monitor MapR services, distributed storage, and cluster computing components.
- Troubleshoot and resolve issues related to stability, performance, and resource utilization.
- Collaborate with cross-functional teams to support data pipelines, Spark workloads, and batch/stream processing.
- Implement monitoring, alerting, and automation using industry-standard tools.
- Contribute to platform roadmaps, documentation, and operational best practices.
Minimum Qualifications- 5+ years of experience in big data engineering, data platform engineering, or infrastructure engineering.
- Strong hands-on experience with Hadoop distributions, specifically HPE MapR.
- Expertise in distributed systems, data storage architectures, and cluster computing.
- Proficiency in Linux/Unix system administration.
- Hands-on experience with at least one programming or scripting language: Python, Java, Scala, or Bash.
- Practical knowledge of Apache Spark, including batch and stream processing paradigms.
- Proven ability to troubleshoot complex issues in large-scale, production-grade environments.
- Experience managing and supporting enterprise clusters requiring high availability.
Preferred Qualifications- Familiarity with monitoring and observability tools such as Grafana, Prometheus, Splunk, or similar solutions.
- Experience with performance tuning and capacity planning for distributed systems.
- Strong documentation skills and the ability to communicate clearly with technical and non-technical teams.
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