Senior Big Data Developer

Job Summary

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 will bring 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

10+ 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: apache,cluster,enterprise data,availability,hadoop,enterprise,linux,security,apache spark,data