Senior Data and Storage Integration Engineer

Resource Management Concepts•Charleston, SC

26d•Onsite

About The Position

RMC is seeking an experienced Senior Data and Storage Integration Engineer to support the Enterprise Systems Department at NIWC Atlantic Service Centers. In this role, you'll drive the design, integration, and optimization of enterprise-level storage solutions while also leveraging advanced analytics and scripting capabilities to solve complex business challenges. We are in search of a highly skilled and experienced Data & Storage Integration Engineer with a strong background in software integration, data science, and enterprise storage systems. The ideal candidate will bring advanced expertise in scripting, data analytics, storage technologies, and enterprise systems to support complex integration, performance, and analytics objectives across a dynamic IT environment. This position is located on-site in either Charleston, SC or New Orleans, LA.

Requirements

Strong background in software integration, data science, and enterprise storage systems
Advanced expertise in scripting, data analytics, storage technologies, and enterprise systems

Responsibilities

Design, deploy, configure, and maintain NetApp storage systems (e.g., FAS, AFF, E-Series, 8200, 8300, A300)
Manage storage protocols (NFS, CIFS/SMB, iSCSI, FC) and ONTAP operating systems
Monitor performance, implement provisioning, and configure storage virtual machines (SVMs)
Apply scripting languages (Python, R) and tools (Jupyter, Pandas, NumPy, Requests) in support of data science or advanced analytics
Develop statistical and operations research models to support business and technical objectives
Design and execute data mining, graph algorithms, or analytics pipelines
Implement SnapMirror, SnapVault, and SnapRestore technologies
Support data backup, disaster recovery planning, and storage consolidation
Integrate with cloud platforms (e.g. AWS, NetApp Cloud Manager, FSxN)
Analyze storage performance metrics and optimize configurations
Implement quality of service (QoS) policies
Troubleshoot storage-related performance issues
Capacity planning and forecasting
Design and implement APIs and ETL pipelines for large-scale data movement
Collaborate with network, server, application, and database teams to meet data delivery requirements
Apply scripting languages (Python, R) and tools (Jupyter, Pandas, NumPy, Requests) to support data science or advanced analytics
Proficiency with operating systems (Linux, Windows Server) and enterprise networking