Engineering - Dallas - Associate, Security Engineering - 10427751

Goldman Sachs; Dallas (TX); On-site; USD 110,000 - 170,000; Full time

Job summary

Goldman Sachs in Dallas, TX is seeking an Associate, Security Engineering to design, develop, and maintain scalable data ingestion and processing pipelines that collect and transform large volumes of security event data for threat detection and analytics.

The role covers batch and streaming workflows, data models and warehouse structures, CI/CD automation, and deploying across on‑prem and AWS with Kubernetes.

Qualifications

• Design, deploy, and maintain data pipelines across on-prem and cloud environments.
• Implement and support distributed data technologies for real-time and batch processing.
• Collaborate with security, analytics, and data science teams to ensure data quality and monitoring.

Responsibilities

• Design, develop, and maintain scalable data pipelines for security telemetry.
• Build batch and streaming workflows for timely data delivery to analytics.
• Develop data models and warehouses to support reporting and data science.
• Create CI/CD pipelines to automate testing, building, and deployment.
• Collaborate with threat management, detection engineering, and analytics teams to configure and monitor data streams.
• Scale data infra across on-prem and AWS using Kubernetes.

Skills

Data pipelines

Python

CI/CD

Security engineering

Real-time processing

Education

Master's degree in Cyber Security / Computer Engineering / Data Science

Bachelor's degree in Cyber Security / Computer Engineering / Data Science

Tools

Spark

Kafka

Kubernetes

BigQuery

AWS

Job description

Job Description

Associate, Security Engineering with Goldman Sachs Services LLC in Dallas, Texas. Design, develop, and maintain scalable data ingestion and processing pipelines to collect, transform, enrich, and load large volumes of structured and semi-structured data from diverse sources into a centralized big data platform, enabling the organization to detect and analyze malicious cyber threats and anomalous activity across its infrastructure. Develop and optimize both batch and streaming data processing workflows to ensure timely and reliable delivery of security event data and telemetry to downstream analytics systems, supporting real-time alerting and incident response capabilities. Design and implement data models, schemas, and data warehouse structures to organize and correlate high-volume event data from multiple sources, supporting analytical reporting, data science initiatives, and operational decision-making. Build and maintain data quality monitoring frameworks, alerting systems, and observability dashboards to ensure pipeline reliability, accuracy, completeness, and performance, guaranteeing that critical data streams remain uninterrupted for continuous monitoring. Develop and maintain Continuous Integration and Continuous Deployment (CI/CD) pipelines to automate the testing, building, and deployment of data engineering applications and infrastructure components, ensuring rapid and reliable delivery of platform updates. Deploy, administer, and scale data systems and infrastructure across on-premises data centers and Amazon Web Services (AWS) cloud environments using container orchestration tools such as Kubernetes to support high-availability, fault-tolerant data processing at scale. Research, evaluate, and implement best-in-class data engineering tools and technologies by conducting Proof-of-Concept activities and recommending solutions that improve platform capabilities, scalability, and efficiency. Collaborate with cross-functional teams — including threat management, detection engineering, data science, and analytics teams — to gather data requirements, define data contracts, and ensure data streams are effectively configured and monitored. Coordinate with product owners and third-party vendor support teams to elicit technical requirements and resolve platform issues. Participate in architecture and design discussions to develop solutions that advance the organization's data infrastructure and analytical maturity for processing and analyzing large-scale enterprise telemetry data.

Job Requirements: Master’s degree (U.S. or foreign equivalent) in Cyber Security, Computer Engineering, Data Science and Analytics, or a related field and one (1) year of experience in the job offered or a related security or data engineering role OR Bachelor’s degree (U.S. or foreign equivalent) in Cyber Security, Computer Engineering, Data Science and Analytics, or a related field and three (3) years of experience in the job offered or a related security or data engineering role. Prior work experience must include one (1) year of experience (with a Master’s degree) or three (3) years of experience (with a Bachelor’s degree) with each of the following: designing, deploying, and maintaining data pipeline systems and ingestion frameworks across on-premise and cloud environments; implementing and supporting distributed data technologies such as Spark, Kafka, Kubernetes, and BigQuery or similar for real-time and batch data processing and analysis; developing automation and tooling using Python to support data monitoring, enrichment, and platform engineering workflows; building and supporting ETL and data ingestion pipelines to centralize large-scale event data into scalable big data infrastructure; implementing and administering AWS cloud infrastructure to enable reliable data processing and analytics capabilities; and collaborating cross-functionally with engineering, analytics, and data science teams to ensure systems and data streams are effectively configured and monitored.

©The Goldman Sachs Group, Inc., 2026. All rights reserved. Goldman Sachs is an equal opportunity employer and does not discriminate on the basis of race, color, religion, sex, national origin, age, veteran status, disability, or any other characteristic protected by applicable law.