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Veeam is building an AI-ready, agentic data governance program designed to make trusted data accessible, reliable, and actionable across the enterprise. As the market leader in data resilience and data security posture management, Veeam is transforming how data governance operates through automation, continuous trust measurement, and intelligent workflows.
You will play a critical hands-on role in designing, building, and operating the technical capabilities behind Veeam's data governance strategy. Reporting directly to the Senior Director of AI-Ready Data Governance, this is a senior individual-contributor position offering significant technical ownership and the opportunity to shape how AI-enabled governance operates across the organization.
Key responsibilities include:
- Design, build, and operate Veeam's AI-ready Data Governance and Trust Operating Process within the Governance Hub, a centralized platform for governance automation, monitoring, and reporting.
- Develop automated data discovery and lineage capabilities to identify authoritative data sources, critical data assets, and key business fields, validating information flows from enterprise systems through the Databricks Lakehouse to downstream analytics and AI applications.
- Build governance workflows that establish clear data ownership, business definitions, usage context, and accountability, collaborating with stakeholders to resolve ambiguities and implement consistent standards across business domains.
- Design and implement continuous, AI-driven data trust scoring systems that evaluate data against defined quality and governance dimensions, determine Trust Tiers, and surface risks, exceptions, and recommended actions.
- Develop automated notification, escalation, and remediation workflows that identify declining data trust, alert accountable owners, and track issues through resolution.
- Lead technical implementation and integration of Databricks Unity Catalog and business-facing data catalog platforms such as Atlan, establishing metadata management, lineage, tagging, access controls, ownership, and certification workflows.
- Build, register, deploy, and maintain AI agents and automated pipelines that perform governance activities within defined security boundaries and operational guardrails.
- Collaborate with business and technical stakeholders across Sales, Marketing, Finance, HR, and Data Engineering to translate governance requirements into scalable, automated solutions.
- Experiment with emerging agentic AI capabilities, including new automation patterns and development tools, evaluating effectiveness and business impact.
- Lead complex, cross-functional governance initiatives from technical design through implementation and ongoing operation.
- Provide technical guidance and mentorship to colleagues and data stewards, promoting reusable automation patterns and governance best practices.
- Continuously improve governance effectiveness by monitoring data trust, automation performance, stakeholder adoption, and operational outcomes.
This is not a traditional policy-administration, catalog-maintenance, or people-management role. You will enjoy building solutions, exploring emerging AI capabilities, and replacing manual governance activities with intelligent automation.
REQUIREMENTS:
- Bachelor's degree in Business, Data Science, Information Systems, a quantitative or related technical discipline, or equivalent practical experience.
- 6+ years of experience in data governance, data engineering, or a closely related technical field, with demonstrated ownership of complex, cross-functional technical initiatives from design through production.
- Proven hands-on experience designing, developing, and deploying production AI agents, automated pipelines, or scripted workflows that replace manual governance activities such as data quality monitoring, trust scoring, lineage mapping, certification checks, or issue detection.
- Advanced technical expertise in Databricks Unity Catalog and business-facing data catalog platforms such as Atlan, including practical implementation of metadata management, data lineage, access controls, tagging, ownership, and governance workflows.
- Strong understanding of AI asset governance principles, including model registration, Model Context Protocol (MCP) service governance, agentic system access controls, and secure AI deployment practices. Experience with Unity AI Gateway is highly desirable.
- Demonstrated proficiency using AI-powered development and automation tools such as Claude, Claude Code, or comparable agentic platforms as part of daily technical workflows, including experience helping others adopt AI-enabled solutions.
- Strong understanding of modern data governance frameworks and operating models, including data ownership, business context, data quality, trust measurement, certification, access management, and automated remediation.
- Proven ability to translate ambiguous business and technical challenges into scalable, production-ready solutions, exercising independent judgment while aligning technical decisions with broader strategic priorities.
- Excellent stakeholder management, collaboration, and influencing skills, with the ability to work effectively across business, engineering, and leadership teams without formal management authority.
- Strong analytical thinking, technical problem-solving, and organizational skills, with experience prioritizing multiple initiatives, managing competing requirements, and delivering measurable business outcomes.
- Experience mentoring colleagues, establishing technical best practices, and driving continuous improvement and adoption of new technologies or automated processes.
Desirable:
- Direct experience with v-Power or comparable CRM extension platforms in Sales Operations or Revenue Intelligence environments.
- Experience leading change management initiatives that transition teams from manual governance activities to automated, agent-driven processes.
- Familiarity with Microsoft Purview for data classification and sensitivity management across Azure and Microsoft 365 environments.
- Knowledge of SOC 2 Type II audit requirements, particularly data processing integrity, access controls, and supporting evidence.
- Experience working in rapidly growing, private equity-backed, or pre-IPO technology organizations.
- Exposure to data mesh architectures, data product operating models, master data management (MDM), or customer and product entity resolution.
- Familiarity with DAMA-DMBOK frameworks or contributions to the broader data platform, governance, or AI infrastructure community.
