Data Virtualization Engineer Jobs in Kentucky (NOW HIRING)

As a Senior Systems Engineer supporting mid‑market and commercial customers, you will play a critical role in driving revenue by owning the technical strategy and execution across multiple concurrent sales opportunities. You will partner closely with sales, channel partners, and technology alliances to understand customer business challenges, design resilient solutions, and clearly articulate differentiated technical value that drives measurable business outcomes.

This role requires strong technical fundamentals, confidence engaging in public‑facing and customer‑facing presentations, and a high level of accountability for progressing deals from discovery through technical validation and close. Successful candidates are comfortable operating in a fast‑paced sales environment, balancing depth of technical engagement with responsiveness, prioritization, and execution.

***Seattle, WA location strong preferred***

• 5+ years of experience in pre‑sales systems engineering, solutions architecture, or related technical customer‑facing roles, supporting mid‑market to enterprise customers.
• Demonstrated ability to manage multiple concurrent sales engagements in a fast‑paced sales environment, balancing deep technical engagement with efficiency, responsiveness, and deal progression.
• Proven experience executing complex sales cycles end‑to‑end, including discovery, solution design, technical validation, and proof‑of‑concept (POC) execution, while owning technical outcomes that directly support deal closure.
• Strong foundational technical background across enterprise infrastructure domains, including data resilience, compute and virtualization platforms, cloud services, networking, and security, with the ability to apply this knowledge to real‑world customer challenges and business objectives.
• Ability to position solutions competitively and articulate differentiated technical value in the context of customer outcomes, cost optimization, risk reduction, and operational resilience.
• Working knowledge of data security, privacy, governance, and regulatory considerations that influence enterprise IT and data protection strategies.
• Foundational understanding of AI, machine learning, and large language models (LLMs), with the ability to explain how trusted, resilient, and well‑governed data enables modern analytics, automation, and AI initiatives.
• Strong verbal and written communication skills, with demonstrated experience presenting technical concepts to IT practitioners, architects, and management‑level audiences, including leading customer‑facing presentations, demonstrations, workshops, and public or semi‑public speaking engagements.
• Proven ability to operate independently with a high level of ownership and accountability, exercising sound judgment within established frameworks while escalating appropriately for high‑impact or ambiguous decisions.
• Collaborative mindset with a track record of partnering closely with sales, channel partners, and technology alliances to advance opportunities, support pipeline generation, and drive technical alignment throughout the sales cycle.
• Demonstrated commitment to team development and knowledge sharing, including mentoring junior colleagues through technical guidance, best practices, and leading by example.

• Containerized and cloud‑native application platforms, including Kubernetes environments and considerations for data protection, backup, and recovery of containerized workloads.
• SaaS application data resilience (e.g., Microsoft 365, Salesforce, and other enterprise SaaS platforms), including understanding shared responsibility models and common recovery use cases.
• API‑driven automation and scripting, including familiarity with REST APIs, basic scripting (PowerShell, Python), or infrastructure‑as‑code concepts used to improve consistency and efficiency.
• Cyber recovery and resilience concepts, including isolated recovery environments, clean‑room architectures, ransomware recovery workflows, and validation of restored data.
• Data governance and policy‑driven protection models, including data classification, retention policies, and alignment with security and compliance requirements.

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