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  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.

You’lllead 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’lllead multiple teams building a searchable, security-aware graph that works atenterprisescale. 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 withhigh standards for quality and security.

• Set the technical vision and end-to-end architecture for the Knowledge Graph, including the data model, storage engine, and query layer atvery 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 andlakehouseintegrations, 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, andBigQuerywithout disrupting production

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

• Own delivery outcomes: roadmap execution, operational readiness, incident learning, and cross-team alignment
• Hire, coach, and develop leaders, including engineering managers and senior/staff engineers, with clear expectations and growth paths

• 10+ years of software engineering experience in data infrastructure, graph systems, or distributed data platforms

• 4+ years of engineering leadership experience, including leading through managers and scaling multiple teams

• Strong production experience with Amazon Neptune and/or Neo4j, including scaling, operations, and trade-offs (property graph vs. RDF)

• Proven ability to lead graph modeling for complex domains, including lineage and permissions at enterprise scale

• Deep knowledge of Gremlin, Cypher/open Cypher, and/or SPARQL, including performance tuning and query design best practices

• Experience with data lakes/lake houses(Delta Lake, Iceberg, Parquet) across major cloud platforms (Azure Data Lake, AWS S3/Glue,Big Query)

• Experience designing andoperatingdistributed systems using tools like Spark, Flink, or Presto/Trino, with strong judgement on scalability and cost

• Strong backend background in Go and/or Python, with the ability to review designs, guide decisions, and unblock teams

• Practical experience using AI-assisted development tools and the ability to set standards that keep AI-assisted code secure and high quality

• Experience operating graph systems at massive scale

• Background in data security, access governance, and policy controls

• Experience with AI/ML governance tools and practices (e.g.,MLflow, Databricks Mosaic AI)

• Experience building custom agents, MCP-based workflows, or reusable engineering automation

• Infrastructure-as-Code experience (e.g., Terraform or Pulumi)

• Contributions to graph standards or communities (GQL,open Cypher, SPARQL)