Sr. Systems Engineer

Veeam is the Data and AI Trust Company, specializing in helping organizations ensure their data and AI are fully understood, secured, and resilient. As a Commercial Pre-Sales Systems Engineer, you will serve as a trusted advisor to enterprise customers, leading complex pre-sales engagements and helping design resilient data platforms that drive business outcomes.

Responsibilities:

• Lead complex pre-sales engagements, including discovery, architecture design, solution positioning, and technical validation
• Act as a trusted advisor to IT practitioners, engineers, architects, and senior leaders by translating business objectives into secure, resilient, and scalable architectures
• Deliver compelling technical demonstrations and decision-maker presentations that clearly articulate business value, cyber risk reduction, and operational outcomes
• Design and manage proofs-of-concept (POCs) to technical closure, aligned to customer success criteria and operational realities
• Collaborate with customers and partners to define architectural strategies supporting data resilience, ransomware recovery, compliance, and hybrid-cloud adoption
• Create tailored proposals and contribute to enterprise-scale RFP and RFx responses
• Maintain strong awareness of industry trends, emerging threats, and competitive positioning to effectively represent Veeam in business discussions
• Partner closely with sales, product management, engineering, and ecosystem partners to influence solution architecture and product direction

Requirements:

• 5+ years of experience in pre-sales systems engineering, solutions architecture, or similar technical leadership roles, with a strong focus on large enterprise environments
• Proven success leading complex, multi-stakeholder sales cycles, including discovery, solution design, POCs, and executive-level engagements
• Strong technical background across data resilience, infrastructure platforms, virtualization, cloud services, networking, and security
• Working knowledge of data security, privacy, and governance, including regulatory and compliance considerations relevant to enterprise customers
• Foundational understanding of AI, machine learning, and large language models (LLMs), with the ability to articulate how trusted, resilient data underpins AI initiatives
• Excellent verbal and written communication skills, with proven public presentation experience delivering compelling messages to executives, senior IT leadership, and large technical or industry audiences
• Self-motivated, high-energy technologist who can learn quickly, adapt, and operate effectively in fast-moving enterprise environments
• Experience with cloud and SaaS platforms, including AWS, Microsoft Azure, and Google Cloud Platform
• Strong working knowledge of Linux and Windows operating systems, including administration, troubleshooting, and performance considerations in enterprise deployments
• Solid understanding of networking fundamentals and architectures, including TCP/IP, DNS, routing, firewalls, load balancing, and network security concepts as they relate to data protection and recovery
• Hands-on experience across enterprise infrastructure domains such as virtualization platforms (VMware vSphere, Microsoft Hyper-V, Nutanix AHV), enterprise storage platforms, identity and access management, and Kubernetes-based environments commonly found in modern hybrid and multi-cloud architectures
• Prior experience with data protection, cyber resilience, or enterprise infrastructure vendors
• Relevant technical or industry certifications (cloud, security, architecture)
• Observability, monitoring, and operational tooling that supports resilience and recovery workflows
• Containerized and cloud-native application platforms, including Kubernetes data protection and recovery considerations
• SaaS application data resilience (e.g., Microsoft 365, Salesforce, and other enterprise SaaS services)
• API-driven automation, scripting, and infrastructure-as-code concepts
• Cyber recovery and clean-room architectures
• Data governance, classification, and policy-driven protection models