• Provide IT support for cloud-based compute and storage environments used to produce protected data products for research and dissemination • Administer distributed cloud platforms, including provisioning, configuration, monitoring, and lifecycle management for high-performance workloads • Support large data processing environments using frameworks such as Apache Spark and optimization tools • Establish and maintain operational parameters for deploying and executing data processing and dissemination workflows • Design and optimize cloud storage configurations, including data curation and removal of redundant datasets • Implement high performance computing (HPC), storage, and workflow optimizations • Deploy and manage monitoring and dashboard solutions to track cluster usage, performance, availability, and cost efficiency • Optimize resource utilization by identifying and terminating idle or unused compute resources • Manage user access, including account provisioning, role-based access, and deprovisioning • Install, configure, and maintain software, operating systems, and platform services, including Linux patching and updates • Produce operational and usage reports related to system performance and data storage • Provide guidance on cloud cost management, budgeting, and optimization strategies • Support agile project execution, including sprint planning, standups, retrospectives, and backlog management • Maintain Kanban or sprint boards and track tasks, milestones, and dependencies • Coordinate communication across engineering, operations, data science, and stakeholder teams • Identify and help resolve technical, operational, and process-related obstacles • Provide guidance on agile best practices and continuous improvement • Support data governance, data quality, and privacy protection requirements • Define and maintain project roles, responsibilities, and reporting structures • Develop and manage project schedules, deliverables, risks, and opportunities • Document and distribute meeting notes, action items, and decisions • Establish data transfer governance, standardized intake, validation, error handling, and repeatable evaluation workflows • Develop and maintain production-ready code, tools, and workflows to support data processing, experimentation, and dissemination, including testing and validation of outputs • Implement, monitor, and optimize cloud compute and storage environments, leveraging automation, infrastructure-as-code, and performance tuning for distributed processing frameworks • Manage source code, system updates, and operational tools, ensuring efficient collaboration, workflow tracking, and reliable execution of production and prototype workloads