• Proactively explore and implement AI tools, LLM integrations, and MCP (Model Context Protocol) to reduce routine database toil, optimize query performance, and accelerate incident resolution. • Support our data warehouse ecosystem by optimizing Snowflake performance, including application packaging and testing. • Own the deep-level optimization of MSSQL (crucial for on-call stability) and PostgreSQL at the server, database, and query levels. • Forecast resource utilization across platforms. Identify cost-saving opportunities, optimize Snowflake credit usage, and right-size AWS infrastructure. • Automate all data infrastructure using Terraform, AWS, Docker, and Kubernetes. You will manage containerized data services and stateful workloads. • Manage and optimize deployment pipelines using GitLab and Octopus Deploy, ensuring safe, repeatable database schema changes. • Create technical documentation, including runbooks, "how-to" guides for developer self-service, and clear architectural diagrams. • Serve as the subject matter expert for SQL Server, Postgres, and Snowflake in a 24/7/365 on-call rotation.

• Deliver the ADO Environment Current-State Assessment Report identifying gaps in configurations, pipelines, and workflow structures (Deliverable A1) • Develop and execute the ADO Configuration Modernization Plan; implement updated ADO configurations including work item hierarchies, custom fields, sprint boards, and Kanban views (Deliverables A2/A3) • Design and deploy reusable CI/CD pipeline templates for Azure Databricks notebook deployment, data validation, and automated reporting (Deliverable A5) • Configure end-to-end DataOps integration: ADO Repos → Databricks notebooks → automated Power BI dashboard refresh — reducing manual effort by 80%+ through workflow automation • Build Power Automate workflows for governance approvals, policy triggers, and document routing integrated with ADO and SharePoint • Design and deploy GMCB's SharePoint Knowledge Management Library including architecture, document taxonomy, metadata schema, and content migration (Deliverables C1/C2) • Develop ADO Analytics dashboards for sprint velocity, governance compliance, data quality, and operational KPIs • Implement traceable work item linkage between ADO Epic-Feature-Story-Task structures and Azure Databricks development artifacts • Develop the ADO Integration Deployment Package including configuration documentation, runbooks, and administrator guides (Deliverable A5) • Support Agile pilot sprints by configuring and validating ADO Board workflows; provide hands-on technical support during adoption phase

• Partner with offshore and onshore engineering teams to design, implement, and scale cloud-native infrastructure supporting a new customer portal and ongoing platform refactoring efforts • Architect, build, and maintain Kubernetes-based environments that power production systems, ensuring scalability, resilience, and security • Lead Infrastructure as Code initiatives (primarily Terraform) to automate provisioning, configuration, and environment consistency across AWS • Design, implement, and optimize CI/CD pipelines to improve deployment velocity, reliability, and developer experience • Integrate and operationalize MLOps practices, enabling efficient deployment, monitoring, and lifecycle management of machine learning workflows • Embed DevSecOps best practices across the platform, incorporating security controls, compliance requirements, and monitoring into the development lifecycle • Drive automation initiatives that reduce manual processes and increase system reliability and repeatability • Collaborate closely with Platform, Engineering, and cross-functional stakeholders to gather requirements, troubleshoot issues, and continuously improve system architecture • Monitor system performance, identify bottlenecks, and proactively implement improvements to optimize availability and cost efficiency • Support incident response and root cause analysis efforts, driving long-term fixes and ensuring lessons learned translate into system improvements

• Running commissioning and provisioning for GPU systems • Running the firmware versions of equipment and components, and communicating the supported versions across the organization • Through Day-2 operations, keeping tight SLOs around efficiency, performance, and availability • Monitoring the hardware state of the cluster, finding bottlenecks and hot spots, and helping users attain peak performance constantly • Triaging the HW break-fix issues and making constant improvements using open-source break-fix solutions • Collaborate with programming and technical divisions to define and implement repeatable procedures • Develop and implement operations strategy & processes, maintaining consistency with SLAs across critically important infrastructure • Develop and apply procedures for minimal downtime and quality controls to strive to achieve continuous uptime • Feeding requirements to software and hardware teams • Creation of documentation that the ecosystem can use to run its own AI Data Centers