Lead Domain Engineering Specialist

Veeam is the Data and AI Trust Company, specializing in helping organizations ensure their data and AI are fully understood, secured, and resilient to enable the acceleration of safe AI at scale. As the market leader in both data resilience and data security posture management, Veeam is built for the convergence of identity, data, security, and AI risk. Headquartered in Seattle with offices in more than 30 countries, Veeam protects over 550,000 customers worldwide, who trust Veeam to keep their businesses running. Join us as we go fearlessly forward together, growing, learning, and making a real impact for some of the world’s biggest brands.

About the Role as a Lead Domain Engineering Specialist:

The ideal candidate will be naturally collaborative, articulate, extremely organized, have a solid technical understanding of Veeam products, and motivated by maximizing customer success and outcomes. Soft skills combined with technical skills are key in this role. You will partner with Customer Success Engineers (CSEs) and Account Executives (AEs) to drive customer outcomes across security-related products and use cases within the Veeam Data Platform (VDP). You’ll lead readiness checks, data modeling, and risk conversations with CISO/CIO stakeholders, while monitoring telemetry and maturity against the Veeam Data Resilience Maturity Model (DRMM) to optimize posture and identify expansion opportunities.

What You’ll Do

Engage customers on security-related products, architectures, and risk topics across the Veeam Data Platform (VDP).

Run readiness checks and lead data modeling to validate solution design and accelerate decisions.

Monitor attack surfaces and vulnerabilities (including DRMM scoring), track telemetry or recurring inspection signals, report trends, and capture potential health checks.

Validate designs to de-risk adoption and accelerate time to value.

Identify and articulate expansion opportunities; review consumption trends and schedule checkpoint reviews (with or without AE coordination).

Engage CISO/CIO stakeholders for risk, status, and opportunity discussions; synthesize inputs from account health and CSE-led QBRs.

Support AEs on security- and AI-driven expansion motions; influence roadmap priorities with CSE counterparts.

Operate as a pooled resource covering Enterprise and Commercial-Named accounts (generally $100K+ ARR), typically at a 1 Security Success Engineer to 6–8 CSE coverage ratio.

Note: Not the primary owner for Onboarding motions nor directly responsible for Renewals

What You’ll Bring

5+ years of experience in engineering or architecting cybersecurity, data security, or AI/ML-driven platforms (e.g., Security Engineer/Architect, Cloud Solution Architect, MLOps/ML Engineer, AI Security Engineer).

Bachelor’s degree in Computer Science, Electrical Engineering, Data Science, or a related technical field; advanced degree preferred.

Relevant certifications (e.g., CompTIA Security+, CISSP, CISM, or equivalent); AI security or governance-related certifications (e.g., AI/ML, privacy, or data governance) are strongly preferred.

Deep expertise in data security and governance , including DSPM and DLP, with demonstrated experience securing data across cloud, SaaS, and hybrid environments.

Hands-on experience with AI security, governance, or emerging AI platforms , including Securiti AI or similar solutions (e.g., AI-SPM, data privacy automation, model governance, or AI risk management frameworks).

Working knowledge of AI/ML architectures and model lifecycles , including training data pipelines, model deployment, inference controls, and associated security risks (e.g., prompt injection, data leakage, adversarial inputs).

Experience with AI/data observability, telemetry, and monitoring across both traditional systems and AI-driven workloads.

Demonstrated ability to engage CISO/CIO/CDO stakeholders on AI risk, data governance, resilience, and enterprise AI adoption strategies.

Hands-on experience with solution design, POCs, and maturity modeling (experience with DRMM or data/AI trust maturity models is a plus).

Familiarity with AI governance frameworks and regulatory landscapes (e.g., NIST AI RMF, EU AI Act, ISO standards) and applying them to enterprise architectures