DevOps Engineer

• Proficiency in Kubernetes and Amazon EKS (2+ years required): Essential for managing containerized applications and ensuring high availability and security in cloud-native environments.
• Strong expertise in AWS serverless technologies (required): Including Lambda, API Gateway, EventBridge, and Step Functions, to build scalable and cost-efficient solutions.
• Hands-on experience with Terraform (2+ years required): Critical for managing Infrastructure as Code (IaC) across multiple environments, ensuring consistency and repeatability.
• CI/CD pipeline development using GitHub Actions (required): Necessary for automating deployments and supporting agile development practices.
• Scripting skills in Python, Bash, or PowerShell (required): Enables automation of operational tasks and enhances infrastructure management capabilities.
• Experience with Databricks and Apache Kafka (preferred): Valuable for teams working with data pipelines, MLOps workflows, and event-driven architectures.

• Design and manage cloud-native infrastructure using container orchestration platforms, ensuring high availability, scalability, and security across environments.
• Implement and maintain Infrastructure as Code (IaC) using tools like Terraform to provision and manage multi-environment cloud resources consistently and efficiently.
• Develop and optimize continuous integration and delivery (CI/CD) pipelines to automate application and infrastructure deployments, supporting agile development cycles.
• Monitor system performance and reliability by configuring observability tools for logging, alerting, and metrics collection, and proactively address operational issues.
• Collaborate with cross-functional teams to align infrastructure solutions with application requirements, ensuring seamless deployment and performance optimization.
• Document technical processes and architectural decisions through runbooks, diagrams, and knowledge-sharing resources to support operational continuity and team onboarding.

• Accelerating data delivery and operational efficiency through automation, observability, and infrastructure-as-code practices that support near real-time data processing and remediation.
• Supporting data integrity and governance by enabling traceable, auditable, and resilient systems that align with regulatory compliance and GenAI readiness.
• Empowering innovation and analytics by maintaining a modular, interoperable platform that integrates internal and third-party data sources for downstream research models, client workflows, and product applications.

Moody's