Big Data Hadoop Engineer

Innoventrics; Charlotte (NC); On-site; USD 120,000 - 150,000; Full time

Job summary

A technology company in Charlotte is seeking a highly skilled Hadoop / HPE MapR Engineer to design and manage large-scale data platforms. This senior-level position requires expertise in Hadoop ecosystems, strong Linux administration, and hands-on experience with Apache Spark. Candidates must have over 7 years in Data Platform Engineering, focusing on ensuring high availability and performance for enterprise workloads. The role involves collaboration with engineering and data teams while ensuring compliance with security standards.

Qualifications

• 7+ years of experience in Big Data / Data Platform Engineering.
• Strong hands-on experience with Hadoop distributions, specifically HPE MapR.
• Deep understanding of distributed systems, cluster computing, and data storage architectures.

Responsibilities

• Design, deploy, and manage large-scale Hadoop clusters with a focus on HPE MapR components.
• Monitor cluster health, troubleshoot performance issues, and conduct root cause analysis.
• Collaborate with engineering, data, and DevOps teams to support enterprise data initiatives.

Skills

Hadoop

Apache Spark

Linux

Data Platform Engineering

Cluster Computing

Security

Tools

HPE MapR

Apache

Docker

Kubernetes

Job description

Job Title: Hadoop / HPE MapR Engineer (Data Platform)

We are seeking a highly skilled Hadoop / HPE MapR Engineer to design, build, operate, and optimize large-scale distributed data platforms. This role is focused on supporting and enhancing HPE MapR–based ecosystems, ensuring high availability, performance, security, and reliability for enterprise data workloads. The ideal candidate brings deep expertise in distributed systems, strong Linux administration skills, and hands‑on experience managing production‑grade Hadoop clusters. This is a senior‑level role requiring ownership of complex initiatives, platform stability, and continuous performance optimization.

Key Responsibilities

• Design, deploy, and manage large-scale Hadoop clusters with a focus on HPE MapR components (MapR‑FS, MapR DB, MapR Streams).
• Administer and optimize distributed data platforms to ensure high availability, fault tolerance, and scalability.
• Monitor cluster health, troubleshoot performance issues, and conduct root cause analysis for production incidents.
• Implement and optimize data processing frameworks including Apache Spark for batch and streaming workloads.
• Perform system-level tuning across Linux/Unix environments (CPU, memory, disk, and network optimization).
• Automate operational tasks using scripting languages such as Python, Bash, or Shell.
• Collaborate with engineering, data, and DevOps teams to support enterprise data initiatives.
• Ensure compliance with enterprise security, governance, and data protection standards.
• Contribute to long‑term platform strategy, capacity planning, and architectural improvements.

Required Qualifications

• 7+ years of experience in Big Data / Data Platform Engineering.
• Strong hands‑on experience with Hadoop distributions, specifically HPE MapR.
• Deep understanding of distributed systems, cluster computing, and data storage architectures.
• Proficiency in Linux/Unix system administration in large-scale environments.
• Hands‑on experience with Apache Spark (batch and/or streaming).
• Strong troubleshooting skills with experience resolving performance and stability issues in production environments.
• Experience with at least one programming/scripting language: Python, Java, Scala, or Bash.
• Solid understanding of cluster monitoring, logging, and incident management processes.

Preferred Qualifications

• Experience with HPE Ezmeral Data Fabric (MapR evolution).
• Exposure to streaming technologies (Kafka or MapR Streams).
• Familiarity with containerization and orchestration (Docker, Kubernetes).
• Experience with CI/CD pipelines and infrastructure automation.
• Knowledge of enterprise data security, Kerberos, or Ranger‑like frameworks.

Skills

• hadoop
• apache
• availability
• enterprise
• apache spark
• security
• linux
• enterprise data
• cluster
• data