IDCS Data Engineer - AI & Infrastructure Enablement

SHI International Corp.•Piscataway, NJ

1d•$120,000 - $145,000•Hybrid

About The Position

This role supports AI development and server rack deployment lifecycle activities, spanning presales design through post‑deployment operations. The focus is on building and maintaining data pipelines, models, and automation that enable AI solutions, rack‑level configurations, demand planning, deployment tracking, and post‑ship optimization. This role bridges AI initiatives with physical infrastructure execution. This is a backfill role with expanded scope and a modernized title aligned to AI‑driven infrastructure delivery.

Requirements

Strong experience in data engineering fundamentals, including ETL/ELT pipelines, data modeling, and cloud‑based data platforms
Hands‑on experience supporting AI or analytics initiatives through data preparation and orchestration
Proficiency in Python and SQL
Experience working with structured and semi‑structured data across operational and engineering systems
Ability to translate presales and operational needs into scalable data solutions
Completed Bachelor’s Degree in Computer Science or a related field, or relevant work experience
Ability to travel 10%

Nice To Haves

Experience supporting AI workloads tied to infrastructure, manufacturing, or data center environments
Familiarity with GenAI, LLM‑adjacent workflows, or analytics supporting deployment automation
Exposure to server, rack, or data center lifecycle data, from design through delivery and post‑deployment
Experience working in presales, services, or solution delivery organizations

Responsibilities

Build and maintain data pipelines that support AI development, analytics, and automation across server and rack deployments
Enable presales activities by supporting data models for solution design, BOM creation, capacity planning, and scenario analysis
Support AI initiatives by preparing and curating data for machine learning, GenAI, and decision‑support use cases
Partner with presales, solutions engineering, IDCS, and operations teams to ensure data continuity from design through deployment and post‑ship support
Integrate data across enterprise systems including configuration tools, inventory systems, manufacturing data, logistics, and deployment tracking
Develop dashboards and analysis that track deployment progress, risks, SLAs, and operational performance
Ensure data quality, reliability, and traceability across the full lifecycle of AI and infrastructure projects
Document standards, repeatable data patterns, and automation to scale AI‑enabled infrastructure delivery