Security Data Engineer (Associate)

Goldman Sachs; Dallas (WV); On-site; USD 130,000 - 190,000; Full time

Job summary

Goldman Sachs Associates in Dallas design, develop, and maintain scalable batch and streaming pipelines to collect, transform, enrich, and load large volumes of security data across on‑premises data centers and AWS cloud environments.

They optimize data models and schemas to support real‑time alerting, incident response, analytics, and data science related to cyber threat detection. They enforce data quality, observability, and CI/CD for automated testing and deployment.

Qualifications

• Master’s or Bachelor’s degree in a related field with relevant security and data engineering focus.

Responsibilities

• Design, develop, and maintain scalable batch and streaming pipelines for large volumes of security data across on‑prem and AWS.
• Optimize data models, schemas, and data warehouse structures for real‑time alerting and analytics.
• Implement data quality monitoring, alerting, and observability for reliable pipelines.
• Develop and maintain CI/CD pipelines to automate testing, deployment and delivery of data apps and infra.
• Deploy and scale data systems with Kubernetes for high availability and fault tolerance.
• Perform security assessments, vulnerability reviews, and crypto‑enabled design choices.
• Collaborate with security teams on telemetry and detection tooling improvements.
• Evaluate data engineering and security tools via PoCs and recommend enhancements.
• Work with cross‑functional teams to define data requirements and resolve platform issues.

Skills

Kafka

Apache Spark

SQL

Python

Kubernetes

CI/CD

Threat modeling

Penetration testing

OWASP CWE

Data modeling

Education

Master’s degree in Cyber Security or Computer Science

Bachelor’s degree in Cyber Security or Computer Science

Tools

Trino

Hive

BigQuery

AWS

On-premises

Job description

Goldman Sachs Associates in Dallas design, develop, and maintain scalable batch and streaming pipelines to collect, transform, enrich, and load large volumes of security data across on‑premises data centers and AWS cloud environments.

They optimize data models and schemas to support real‑time alerting, incident response, analytics, and data science related to cyber threat detection. They enforce data quality, observability, and CI/CD for automated testing and deployment.