Systems Engineering Associate: Cloud, Automation & Reliability

Goldman Sachs; New York (NY); On-site; USD 156,000 - 192,000; Full time

Job summary

Goldman Sachs is seeking an Associate, Systems Engineering in New York, NY. The role involves developing expertise across datacenter design, networks, storage, grid computing, operating systems, and databases, and contributing to design and development of the firm’s infrastructure with public and private clouds for on-demand scaling.

Responsibilities include automated testing, data mining and machine learning to identify issues, monitoring of application health, and escalation with stakeholders.

Qualifications

• Bachelor’s degree in Computer Science, Computer Engineering, Information Systems/Technology, or related field.
• Three years of experience in performance tuning, troubleshooting, or log analysis.
• Experience with UNIX, Shell scripting, Python, and SDLC.

Responsibilities

• Develop expertise across Engineering domains including datacenter design, networks, storage, grid computing, operating systems, databases, and software languages.
• Identify and implement public and private cloud-based solutions to scale applications on demand.
• Create automated testing processes, standards, solutions and tools to ensure smooth operation.
• Review production incidents and communicate with stakeholders; monitor risk and drive incident resolution.

Skills

Performance tuning

Troubleshooting

Python

UNIX

SQL analysis

Education

Bachelor’s degree in Computer Science / Computer Engineering / Information Systems

Tools

Oracle

DB2

Sybase ASE

Unix

Shell scripting

Job description

Goldman Sachs is seeking an Associate, Systems Engineering in New York, NY. The role involves developing expertise across datacenter design, networks, storage, grid computing, operating systems, and databases, and contributing to design and development of the firm’s infrastructure with public and private clouds for on-demand scaling.

Responsibilities include automated testing, data mining and machine learning to identify issues, monitoring of application health, and escalation with stakeholders.