You will be a key addition to GCDI Engineering’s Platform team which operates in multiple capacities across a variety of internally developed technologies and vendor products, with focus on major areas:
Data Ingestion Engineering - we collect security related events and data feeds into a centralized big data infrastructure. We research, implement and support best in class technology solutions related to data ingestion, transformation, enrichments, and analysis. Technologies include but are not limited to Kafka, Spark, Kubernetes, and BigQuery.
Security Platform Engineering – we research, implement, and support the platforms and tooling leveraged by other teams within GCDI to perform security automation and response, real-time and scheduled log analysis, data science modeling, and customized SDLC workflows.
HOW YOU WILL FULFILL YOUR POTENTIAL
As an Engineer in Platform Engineering, you will be an integral part of a technical team that is responsible for providing the GCDI organization with security sensors and data sets that increase awareness of current and potential Cyber Threats. The Platfrom engineering team Manages the core infrastructure that powers GCDI, providing a secure, scalable, and resilient foundation that enables the Data Engineering team to ingest and process security event logs effectively. You will work closely with Threat Management Centre, Hunt, Detection Engineering to ensure our sensors and data streams are effectively configured, streamlined, integrated, automated and monitored to ensure the systems and data necessary to protect the firm from cyber threats is available.
You possess the aptitude to work across teams and product owners, to elicit tool-related requirements from all parts of Technology Risk, and to work with tool vendor support teams in resolving issues that may arise.
- Contribute to multi-cloud platform architecture design and implementation (AWS primary, Azure/GCP secondary).
- Implement network topologies, IAM roles/policies, and account boundaries.
- Provision and manage cloud resources using Infrastructure as Code (e.g., Terraform, Ansible) and Git-based workflows.
- Participate in architecture, design, and change request reviews.
- Deploy, configure, and operate Kubernetes clusters.
- Configure and maintain core Kubernetes platform services (ingress, load balancing, storage, autoscaling).
- Implement and maintain Kubernetes RBAC, namespace structures, and basic network policies.
- Troubleshoot Kubernetes workloads and cluster-level issues.
- Build and maintain container images following established standards.
- Manage and operate container registries and image promotion workflows within CI/CD pipelines.
- Integrate and operate CNCF ecosystem tools for observability (Prometheus, Grafana, Loki, FluentBit, OpenTelemetry) and deployment/configuration (Helm, GitOps tools).
- Contribute to the adoption of GitOps workflows for application deployments and platform configuration management.
- Support deployment and operation of data and streaming platforms on Kubernetes (e.g., Apache Spark, Flink, Kafka).
- Implement storage and configuration for stateful workloads (persistence, backup, basic performance tuning).
- Collaborate with data engineering teams to configure resources, optimize job execution, and improve reliability.
- Operate and troubleshoot Linux-based systems and Kubernetes worker nodes.
- Implement hardening baselines and security configurations on Linux hosts.
- Automate OS-level configuration, patching, and maintenance (e.g., Bash, Python, Packer, Ansible).
- Integrate security checks into CI/CD pipelines (container image, dependency, configuration/policy scanning).
- Apply policy-as-code tools (e.g., OPA, Kyverno) to enforce platform guardrails.
- Implement least-privilege IAM roles, encryption standards, and secure secrets management.
- Participate in incident response and post-incident reviews for platform-related security/reliability events.
- Implement and manage observability tooling for metrics, logs, and traces across cloud and Kubernetes.
- Build and maintain dashboards, alerts, and runbooks for common scenarios.
- Contribute to defining and monitoring Service Level Indicators (SLIs) and SLOs.
- Assist in capacity planning, performance testing, and cost optimization initiatives.
- Develop and maintain reusable automation (modules, pipelines, scripts) for infrastructure provisioning and application deployment.
- Implement CI/CD pipelines (e.g., GitLab CI, Harness) for automated build, test, security scanning, and deployment.
- Help build self-service capabilities for development and data teams (templates, APIs).
- Identify manual operational tasks and reduce toil through scripting and automation.
- Familiarity with AIOps concepts, including ML-based anomaly detection for infrastructure metrics, logs, and security events
- Experience with predictive analytics for capacity planning, resource utilization, and failure prediction
- Understanding of automated root cause analysis using AI-driven correlation tools
- Familiarity with provisioning and managing GPU-enabled workloads for ML training/inference
- Understanding of ML feature pipelines using Spark, Kafka, and BigQuery
- Experience with AI-assisted automation for infrastructure provisioning and self-healing systems
- Familiarity with AI-powered CI/CD optimization, intelligent test selection, and deployment risk assessment
- Understanding of ML-driven auto-scaling and self-healing mechanisms in Kubernetes environments
- Collaborate with engineering, data, SRE, and security teams to translate requirements into solutions.
- Document designs, runbooks, and standards; contribute to internal knowledge bases.
- Participate in internal training, demos, and brown-bag sessions to promote best practices.
- Provide guidance and informal mentoring to junior engineers.
- Bachelor’s degree in Computer Science, Engineering, or related technical field, or equivalent practical experience.
- 4+ years of experience in infrastructure, platform, or cloud engineering roles, with hands-on experience in production environments.
- Strong practical experience with AWS services such as:
- VPC networking, security groups, and routing.
- IAM (roles, policies, federation).
- EC2, EKS, RDS, S3, SQS, SNS, CloudWatch, and related services.
- Solid experience running workloads on Kubernetes in production, including:
- Deploying and managing workloads (Deployments, StatefulSets, DaemonSets, Jobs).
- Configuring services, ingress, ConfigMaps, and Secrets.
- Understanding of cluster networking, storage concepts, and basic security controls (RBAC, NetworkPolicies).
- Strong hands-on experience with containerization:
- Building secure and efficient container images.
- Working with container registries and image promotion/tagging strategies.
- Strong Linux systems skills:
- System administration, networking basics, shell scripting, and troubleshooting.
- Automation using Bash and at least one higher-level language (e.g., Python or Go).
- Practical experience with DevSecOps practices:
- Implementing CI/CD pipelines (e.g., GitLab CI, Harness Pipelines, or similar).
- Integrating security and quality checks into build and deployment pipelines.
- Infrastructure as code using tools such as Terraform, Ansible, or CloudFormation.
- Hands-on experience with observability tooling:
- Metrics, logging, and tracing for distributed applications and Kubernetes (e.g., Prometheus, Grafana, Loki, FluentBit, OpenTelemetry).
- Creation of dashboards, alerts, and basic runbooks.
- Strong problem-solving and troubleshooting skills in distributed, cloud-native environments.
- Effective communication and collaboration skills, with the ability to work cross-functionally with engineering, data, and security teams.
What We Do
At Goldman Sachs, our Engineers don’t just make things – we make things possible. Change the world by connecting people and capital with ideas. Solve the most challenging and pressing engineering problems for our clients. Join our engineering teams that build massively scalable software and systems, architect low latency infrastructure solutions, proactively guard against cyber threats, and leverage machine learning alongside financial engineering to continuously turn data into action. Create new businesses, transform finance, and explore a world of opportunity at the speed of markets.
Engineering, which is comprised of our Technology Division and global strategists groups, is at the critical center of our business, and our dynamic environment requires innovative strategic thinking and immediate, real solutions. Want to push the limit of digital possibilities? Start here.
Who We Look For
Goldman Sachs Engineers are innovators and problem-solvers, building solutions in risk management, big data, mobile and more. We look for creative collaborators who evolve, adapt to change and thrive in a fast-paced global environment.
ABOUT GOLDMAN SACHS
At Goldman Sachs, we commit our people, capital and ideas to help our clients, shareholders and the communities we serve to grow. Founded in 1869, we are a leading global investment banking, securities and investment management firm. Headquartered in New York, we maintain offices around the world.
We believe who you are makes you better at what you do. We're committed to fostering and advancing diversity and inclusion in our own workplace and beyond by ensuring every individual within our firm has a number of opportunities to grow professionally and personally, from our training and development opportunities and firmwide networks to benefits, wellness and personal finance offerings and mindfulness programs. about our culture, benefits, and people at GS.com/careers.
We’re committed to finding reasonable accommodations for candidates with special needs or disabilities during our recruiting process. : https://www.goldmansachs.com/careers/footer/disability-statement.html
© The Goldman Sachs Group, Inc., 2023. 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, veterans status, disability, or any other characteristic protected by applicable law.
