Engineering - Dallas - Associate, Security Engineering - 10427751

Goldman Sachs•Dallas, TX

16h•Onsite

About The Position

Goldman Sachs Services LLC is seeking an Associate, Security Engineering in Dallas, Texas. This role involves designing, developing, and maintaining scalable data ingestion and processing pipelines to collect, transform, enrich, and load large volumes of structured and semi-structured data into a centralized big data platform. The goal is to enable the organization to detect and analyze malicious cyber threats and anomalous activity across its infrastructure. The position requires developing and optimizing both batch and streaming data processing workflows for timely and reliable delivery of security event data and telemetry to downstream analytics systems, supporting real-time alerting and incident response. It also includes designing and implementing data models, schemas, and data warehouse structures to organize and correlate high-volume event data, supporting analytical reporting, data science initiatives, and operational decision-making. Building and maintaining data quality monitoring frameworks, alerting systems, and observability dashboards are crucial for ensuring pipeline reliability, accuracy, completeness, and performance. The role also involves developing and maintaining CI/CD pipelines for automated testing, building, and deployment of data engineering applications and infrastructure components. Deployment, administration, and scaling of data systems and infrastructure across on-premises data centers and AWS cloud environments using Kubernetes are expected. Researching, evaluating, and implementing best-in-class data engineering tools and technologies through Proof-of-Concept activities is also part of the role. Collaboration with cross-functional teams, including threat management, detection engineering, data science, and analytics teams, is essential for gathering data requirements, defining data contracts, and ensuring effective configuration and monitoring of data streams. Coordination with product owners and third-party vendor support teams to elicit technical requirements and resolve platform issues is also required. Participation in architecture and design discussions to advance the organization's data infrastructure and analytical maturity for processing and analyzing large-scale enterprise telemetry data is expected.

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.
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.
Collaborating cross-functionally with engineering, analytics, and data science teams to ensure systems and data streams are effectively configured and monitored.

Responsibilities

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.
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.
Design and implement data models, schemas, and data warehouse structures to organize and correlate high-volume event data from multiple sources.
Build and maintain data quality monitoring frameworks, alerting systems, and observability dashboards.
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.
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.
Research, evaluate, and implement best-in-class data engineering tools and technologies by conducting Proof-of-Concept activities and recommending solutions.
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.