Security Data Engineer: Real-Time Pipelines & Analytics

Socket.dev; Dallas (TX); On-site; USD 120,000 - 170,000; Full time

Job summary

Goldman Sachs is seeking an Associate, Security Engineering in Dallas, TX, to design and maintain scalable data ingestion pipelines that collect, transform, enrich, and load large volumes of structured and semi-structured data from diverse sources into a centralized big data platform for threat detection and anomaly analysis.

The role includes building batch and streaming workflows for real‑time alerting, designing data models and warehouse structures, and establishing data quality monitoring,

Qualifications

• Master’s degree in Cyber Security, Computer Engineering, Data Science and Analytics, or related field with 1 year of experience
• Or Bachelor’s degree with 3 years of experience in security or data engineering
• Experience designing, deploying, and maintaining data pipeline systems across on‑prem and cloud environments

Responsibilities

• Design, develop, and maintain scalable data ingestion and processing pipelines for large volumes of data
• Build batch and streaming workflows for timely delivery of security event data
• Develop data models, schemas, and data warehouse structures for analytics
• Create data quality monitoring, alerting, and observability dashboards
• Develop CI/CD pipelines to automate testing, building, and deployment of data applications
• Scale data systems across on‑prem and AWS cloud with Kubernetes
• Collaborate with threat management, data science, and analytics teams
• Engage with product owners and vendors to elicit requirements and resolve issues
• Contribute to architecture discussions to improve data infrastructure

Skills

Data pipelines

Python

CI/CD

Collaboration

Education

Master’s degree in Cyber Security/Comp Eng/Data Science

Bachelor’s degree in Cyber Security/Comp Eng/Data Science

Tools

Kafka

Spark

Kubernetes

BigQuery

Python

Job description

Goldman Sachs is seeking an Associate, Security Engineering in Dallas, TX, to design and maintain scalable data ingestion pipelines that collect, transform, enrich, and load large volumes of structured and semi-structured data from diverse sources into a centralized big data platform for threat detection and anomaly analysis.

The role includes building batch and streaming workflows for real‑time alerting, designing data models and warehouse structures, and establishing data quality monitoring,