Drimva

Apple

Controls Engineer, Data Center Engineering

Dallas, Texas +4 · Full-time · Posted Jul 27, 2026

Location
Dallas, Texas
Maiden, North Carolina
Mesa, Arizona
Prineville, Oregon
Sparks, Nevada
Experience
5+ years required; preferred qualification states 8+ years.
Education
Bachelor's degree in Electrical Engineering, Mechanical Engineering, Systems Engineering, Controls Engineering, or a related field, or equivalent job-related experience.

About the job

Designs, deploys, commissions, and improves integrated electrical and mechanical control systems for Apple data centers, including their monitoring platform.

What you'll do

  • Design and review electrical and mechanical automation systems, specifications, and sequences of operations.
  • Program or oversee controllers, integrate system data into the Ignition-based DCIS, and oversee commissioning and troubleshooting.
  • Coordinate with project stakeholders and support operations through training, mentorship, and incident response.

What they ask for

  • Bachelor's degree in Electrical Engineering, Mechanical Engineering, Systems Engineering, Controls Engineering, or a related field, or equivalent job-related experience.
  • 5+ years of engineering experience with control systems in data centers or mission-critical environments for both electrical power and mechanical/HVAC systems.
  • Understanding of controls for high-availability electrical and mechanical systems.
  • Experience programming DDC/PLC controllers and redlining controls drawings.

Nice to have

  • 8+ years of data center controls experience spanning design, construction, and commissioning; specification authorship experience.
  • Experience with named control platforms, communication protocols, DCIS technologies, AI tools, or AI/ML analytics.

Tools

EPMS, BAS, DDC, PLC, MQTT, Inductive Automation's Ignition, BACnet, Modbus

AI focus

Preferred proficiency using AI for specification and sequence authoring, control-logic and Python development, and applying AI/ML analytics to operational data for predictive fault detection, anomaly detection, and performance optimization.

Model-written summary · the original posting is authoritative

First seen Oct 15, 2026 · Last verified Nov 5, 2026 (UTC)