Sr. Director, Graph Databases

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

You’ll lead two core systems inside Veeam Data Command Center: the Knowledge Graph and the hyperscale data lake integrations. Together, they help customers understand where sensitive data lives, who can access it, how it moves, and whether AI models trained on it can be trusted.

You’ll lead multiple teams building a searchable, security-aware graph that works at enterprise scale. This role is for a hands-on technical leader who can set clear direction, grow strong teams, and deliver reliable systems—while building an AI-first engineering culture with high standards for quality and security.

What You’ll Do

• Set the technical vision and end-to-end architecture for the Knowledge Graph, including the data model, storage engine, and query layer at very large scale

• Guide the evolution of the graph schema for data sources, identities, access, classifications, and lineage (property graph and/or RDF) using Amazon Neptune and/or Neo4j

• Own the strategy for hyperscale lake and lakehouse integrations, including connectors and scanning engines that ingest metadata and lineage from Delta Lake, Iceberg, Parquet/Avro, and platforms like Azure Data Lake, AWS S3/Glue, and BigQuery without disrupting production

• Drive performance and reliability, including standards for indexing, partitioning, and query planning, and tuning traversals and queries (Gremlin, Cypher/openCypher, SPARQL)

• Build and scale an AI-first engineering approach where teams use tools like Claude Code, Cursor, and Copilot responsibly, with guardrails for security, maintainability, and code quality

• Invest in reusable engineering building blocks (including “Claude skills” and agent workflows) that make teams faster and more consistent

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