Jobs at Veeam

Principal Product Manager responsible for defining and launching data and AI trust products spanning data resilience, security, AI governance, and Kubernetes workloads. The role leads 0-to-1 product initiatives, partners with engineering and customers, drives security and compliance, develops roadmaps and success metrics, supports sales demonstrations, and strengthens ecosystem partnerships. It requires strong technical fluency in AI, cloud infrastructure, cybersecurity, data protection, and recovery, along with the ability to influence cross-functional teams without direct authority.

Designs and operates secure, highly scalable cloud-native infrastructure across AWS, Azure, and GCP. Responsibilities include infrastructure as code, Kubernetes administration, GitOps-based CI/CD, zero-downtime deployments, observability, distributed-systems troubleshooting, on-call production support, and automation using Python and Go. The role also requires database operations, Linux and networking expertise, service mesh knowledge, and implementation of zero-trust and least-privilege security practices.

Design and build scalable testing frameworks, performance and stress-testing harnesses, distributed load-generation tools, and fault-injection pipelines for high-scale data connectors. Validate ingestion workflows across AWS, Azure, Kubernetes, streaming systems, queues, and APIs under extreme concurrency, degraded networks, schema changes, and authentication failures.

Coordinate end-to-end delivery of complex Forward Deployed Engineering projects involving data governance, privacy, and DataAI Security. Manage milestones, risks, dependencies, stakeholder alignment, customer adoption, and executive communications. Support technical teams by providing delivery structure, translating technical progress into business updates, and driving timely decisions across technical, business, legal, privacy, and executive stakeholders.

Develop and optimize high-scale data ingestion pipelines for throughput, latency, scalability, and resource efficiency. Design performance tests, generate realistic datasets, diagnose full-stack bottlenecks across Go, Kubernetes, cloud, networking, and databases, and implement fixes involving concurrency, batching, back-pressure, and resource reuse. Analyze production telemetry, build profiling and regression tools, tune PostgreSQL, Redis, and Databricks workloads, resolve customer performance issues, and guide performant system design.

Build and maintain secure, reliable cloud infrastructure and an internal developer platform for Veeam Data Cloud. Automate infrastructure using code, collaborate with engineers, document systems and processes, support containers and serverless services, and participate in on-call product operations. The role requires SaaS production operations experience, cloud development skills, infrastructure-as-code expertise, GitHub Actions, public cloud knowledge, secure design principles, and familiarity with AI coding tools.

Leads and mentors an engineering team building Veeam Kasten features for Kubernetes backup, recovery, and application mobility. Responsibilities include designing, developing in Go, testing, documenting, and delivering scalable concurrent systems; collaborating with product and engineering teams; and contributing to cloud-native open-source communities. The role requires engineering management experience, strong software development expertise, computer science fundamentals, and familiarity with Kubernetes and related container technologies.

Leads and develops a team of Forward Deployed Engineers delivering enterprise deployments across multiple accounts. Owns delivery planning, milestones, risks, customer outcomes, workload allocation, escalations, architecture reviews, technical standards, and executive stakeholder communication. Partners cross-functionally with Sales, Product, Engineering, Customer Success, and Professional Services while improving deployment patterns, automation, and team effectiveness across cloud, hybrid, and on-premises environments.

Own technical delivery for complex enterprise customers across deployment, integration, automation, and solution architecture. Lead hybrid cloud, on-premises, Kubernetes, container, and Linux deployments; build integrations, agents, tools, workflows, and reusable playbooks; resolve complex technical escalations; and translate data, security, privacy, and AI needs into scalable solutions. Partner with executives and technical stakeholders, mentor other engineers, and collaborate with Sales Engineering, Product, and Engineering on strategic customer initiatives.

Own strategic customer deployments of the Securiti AI platform across cloud, hybrid, and on-premises environments. Build integrations, automations, data pipelines, classifiers, and agent workflows; configure Kubernetes and cloud infrastructure; troubleshoot complex deployment and integration issues; translate security, privacy, governance, and AI objectives into technical solutions; and lead customer technical sessions while partnering with Sales Engineering, Product, and Engineering.

Lead system-level and infrastructure testing in a San Jose lab. Build end-to-end test environments spanning on-premises hardware, Kubernetes, and cloud edge topologies. Develop offline validation harnesses, profile operating-system resources, execute hardware-in-the-loop automation, and maintain physical and virtual testbeds. Own automated orchestration for resetting, patching, and reconfiguring test rigs while ensuring platform stability, hardware-software integration, and deployment verification.

Lead quality strategy and automated testing for graph database architectures and high-volume reporting engines. Build test suites for graph traversals, indexing, data fidelity, aggregation accuracy, latency, and schema evolution. Design benchmarking platforms for query and memory performance, and use Kubernetes across AWS and Azure to create isolated multi-tenant database environments for regression testing.

Design and scale distributed data processing and analytics platforms supporting OLTP, OLAP, streaming, batch workloads, and AI-driven decisioning. Build reliable pipelines and integrate cloud data warehouses, lakehouses, and processing systems handling petabyte-scale data. Optimize latency, throughput, reliability, observability, and cost efficiency while collaborating with data scientists, ML engineers, and product teams.

Summer 2027 Policy Engineering Intern developing Go backend code, internal tooling, testing harnesses, AI-assisted developer utilities, and evaluation frameworks. The intern will contribute to a production cloud data governance platform, collaborate with senior engineers through design and code reviews, and gain experience with distributed systems, event-driven architecture, multi-tenant SaaS, and cloud infrastructure. This is a paid, 10-week, fully onsite internship in San Jose, California.

Train and evaluate machine learning and deep learning models, diagnose training and generalization issues, conduct experiments, and develop reusable research tooling. The intern may work on LLMs, transformers, generative AI, fine-tuning, and evaluation workflows while rapidly prototyping with AI-assisted development tools. The role requires advanced quantitative coursework and graduate study in computer science, AI, machine learning, statistics, applied mathematics, data science, electrical engineering, or a related field.

Own the vision, roadmap, and customer experience for Veeam’s Knowledge Graph, reporting, dashboards, analytics, and evidence capabilities. Translate complex graph data into actionable views for security, privacy, governance, and executive users. Lead metrics, reporting quality, APIs, BI integrations, AI-agent consumption, query experiences, and enterprise-scale performance requirements. Partner with engineering, design, AI, marketing, and support while conducting enterprise customer discovery and using AI-assisted tools to accelerate product development.

Own product strategy, roadmap, execution, pricing, packaging, and go-to-market input for Veeam’s Data and AI Security portfolio. Lead and develop the product management team, align Engineering, Design, Marketing, and Sales, and represent strategy to the CEO and executive team. Guide data security posture management and AI governance priorities using customer insights, competitive analysis, data, and judgment. Integrate an acquired product line into Veeam’s broader platform.

Own product marketing strategy for Veeam Intelligent ResOps, including positioning, messaging, launches, campaigns, sales enablement, competitive strategy, analyst relations, pricing, packaging, and route-to-market support. Track pipeline, win rate, and revenue to guide business decisions. Partner with product, sales, finance, channel, alliance, and executive teams while shaping market direction and customer-driven product strategy.

Own product marketing for Veeam DataAI Privacy, including positioning, competitive differentiation, regulatory messaging, product launches, campaigns, sales enablement, analyst engagement, and public speaking. Translate privacy and AI regulations into customer and seller value, inform product and pricing decisions with market insights, and measure impact on pipeline and win rates. Partner closely with product, sales, marketing, executives, customers, and analysts.

Lead hyperscaler integration strategy (AWS, Azure, GCP) for Veeam, represent partnerships, influence cross-product roadmaps, mentor product advisors, resolve complex integration escalations, advocate architectural standards, and engage industry stakeholders through presentations and advisory work.