Systems Engineering Associate: Cloud & Automation

The Goldman Sachs Group; Draper (UT); On-site; USD 110,000 - 160,000; Full time

Job summary

The Goldman Sachs Group is seeking an Associate, Systems Engineering in Draper, Utah to help design and optimize the firm’s computer and data infrastructure. You will work across datacenter design, networks, storage, and grid compute while implementing cloud-based solutions for on‑demand scaling.

You will also contribute to automated testing, ML-driven anomaly detection, and ongoing monitoring of application health and risk in production environments.

Qualifications

• Master's or Bachelor's degree in a relevant field with required experience.
• Experience with performance tuning and troubleshooting application issues using Java, Python, and UNIX.
• Experience with public cloud platforms (AWS, GCP) and SQL performance tuning.
• Experience with MongoDB or similar databases and SDLC processes.

Responsibilities

• Develop expertise across data center design, networks, storage, and grid compute.
• Identify internal hardware and implement cloud solutions for on‑demand scaling.
• Automate testing processes, standards, and tooling.
• Apply data mining and ML techniques to detect infrastructure issues and trends.
• Monitor application health, performance, and risk in production environments.
• Support SDLC activities from requirements through release and support.

Skills

Java

Python

Shell scripting

UNIX

MongoDB

SQL performance tuning

SDLC

Cloud troubleshooting

Code debugging

Log analysis

AWS/GCP

Education

Master's degree in Computer Science/Information Technology or related field

Bachelor's degree in Computer Science/Information Technology or related field

Job description

The Goldman Sachs Group is seeking an Associate, Systems Engineering in Draper, Utah to help design and optimize the firm’s computer and data infrastructure. You will work across datacenter design, networks, storage, and grid compute while implementing cloud-based solutions for on‑demand scaling.

You will also contribute to automated testing, ML-driven anomaly detection, and ongoing monitoring of application health and risk in production environments.