Associate, Systems Engineering — Cloud & Data Infra

Goldman Sachs; New York (NY); On-site; USD 138,000 - 166,000; Full time

Job summary

Goldman Sachs is seeking an Associate, Systems Engineering in New York, NY to develop expertise across engineering domains, including datacenter design, networks, storage, grid compute, operating systems, databases, and cloud-based solutions to enable on-demand scaling of the firm's applications.

You will contribute to automated testing, data mining, incident review, and performance monitoring, while applying ML techniques to automate failures and improve stability, disaster recovery planning,

Qualifications

• Bachelor’s degree or foreign equivalent in CS/CE/EE or related field with three years of experience in role.
• Experience with performance tuning and troubleshooting application issues using Oracle, code debugging, or log analysis.
• SDLC experience including requirements, design, coding, testing, release and support.

Responsibilities

• Develop expertise across engineering domains including datacenter design, networks, storage, grid compute, OS, databases, and cloud solutions.
• Identify and resolve application issues, document repetitive resolutions, and support service performance.
• Automate testing, monitoring, and reporting to improve stability and incident response.

Skills

Performance tuning

Troubleshooting

UNIX shell scripting

Python

SQL performance tuning

SDLC

Windows systems management

Education

Bachelor’s degree in Computer Science, Computer Engineering, Electronic Engineering, or related field

Tools

Oracle

DB2

Sybase ASE

Microsoft Configuration Manager

Job description

Goldman Sachs is seeking an Associate, Systems Engineering in New York, NY to develop expertise across engineering domains, including datacenter design, networks, storage, grid compute, operating systems, databases, and cloud-based solutions to enable on-demand scaling of the firm's applications.

You will contribute to automated testing, data mining, incident review, and performance monitoring, while applying ML techniques to automate failures and improve stability, disaster recovery planning,