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About Goldman Sachs
Goldman Sachs is a leading global investment banking, securities, and investment management firm providing financial services to corporations, financial institutions, governments, and individuals.
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Associate, Security Engineering - 10427751 joins the Engineering Division to design, develop, and maintain scalable data ingestion and processing pipelines for a centralized big data platform. The role involves building batch and streaming workflows to collect, transform, and enrich large volumes of structured and semi-structured data to detect malicious cyber threats and anomalous activity across infrastructure. Key responsibilities include creating data models, monitoring frameworks, and CI/CD pipelines while managing data systems in on-premises and AWS cloud environments using Kubernetes. The position requires proficiency in Python, Spark, Kafka, and BigQuery or similar distributed technologies. Collaborating with cross-functional teams like threat management and data science, the role focuses on solving complex problems related to processing large-scale enterprise telemetry data to support real-time alerting, incident response, and advanced analytical reporting for security operations.
Design and maintain scalable data ingestion pipelines to collect and process large volumes of structured and semi-structured security data.
Develop and optimize batch and streaming data processing workflows for real-time alerting and incident response.
Create data models, schemas, and warehouse structures to organize high-volume event data for analytics and reporting.
Build monitoring frameworks, alerting systems, and observability dashboards to ensure pipeline reliability and performance.
Maintain CI/CD pipelines to automate the testing, building, and deployment of data engineering applications.
Deploy and manage data infrastructure across on-premises data centers and AWS cloud environments using Kubernetes.
Conduct Proof-of-Concept activities to research and implement best-in-class data engineering tools and technologies.
What we're looking for
A Master's degree in Cyber Security, Computer Engineering, Data Science and Analytics, or a related field and one year of relevant experience is required.
A Bachelor's degree in Cyber Security, Computer Engineering, Data Science and Analytics, or a related field and three years of relevant experience is required.
Experience designing, deploying, and maintaining data pipeline systems and ingestion frameworks across on-premise and cloud environments is required.
Experience implementing and supporting distributed data technologies such as Spark, Kafka, Kubernetes, and BigQuery for real-time and batch processing is required.
Experience developing automation and tooling using Python to support data monitoring, enrichment, and platform engineering workflows is required.
Experience building and supporting ETL and data ingestion pipelines to centralize large-scale event data into scalable big data infrastructure is required.
Experience implementing and administering AWS cloud infrastructure to enable reliable data processing and analytics capabilities is required.
Experience collaborating cross-functionally with engineering, analytics, and data science teams to ensure systems are effectively configured and monitored is required.