The job, in the employer's words
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
- 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
What You’ll Bring
- 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/openCypher, and/or SPARQL, including performance tuning and query design best practices
- Experience with data lakes/lakehouses (Delta Lake, Iceberg, Parquet) across major cloud platforms (Azure Data Lake, AWS S3/Glue, BigQuery)
- Experience designing and operating distributed 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
Bonus Skills
- 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, openCypher, SPARQL)
What you'll get
- Unlimited paid time off, 12 paid holidays including 4 global VeeaMe Days for self-care and 24 paid volunteer hours annually through Veeam Cares
- Paid parental leave: 8 weeks for all parents, 16 weeks for birthing parents
- Medical, dental, and vision coverage starting on your first day
- Mental health support, therapy sessions, and digital wellness tools via our Employee Assistance Program
- 401(k) retirement plan with company matching contributions
- Fertility, adoption, and surrogacy support through Maven, plus paid volunteer time
- AirVet: 24/7 virtual veterinary care at no cost
- Legal services, identity protection, and supplemental health insurance options
- Tax-advantaged spending accounts for healthcare, dependent care, and commuting
- Opportunities to learn and grow through on-demand libraries (LinkedIn Learning, O’Reilly), mentoring, workshops, and learning events like our annual Global Day of Learning
Pay Transparency
Veeam is committed to pay transparency and equitable compensation. For this role, the compensation range below reflects the expected total target compensation (TTC), inclusive of base pay and a competitive performance-based bonus. For roles with a commission plan, the compensation range represents On Target Earnings (OTE), which includes base salary plus variable commission. When determining compensation, Veeam takes into consideration factors such as experience, education, skills, and geographic zone. Offers are typically made below the midpoint of the range.
In addition to compensation, Veeam provides a comprehensive benefits package, including health coverage, retirement plans, and unlimited time off.
Veeam Software is an equal opportunity employer and does not tolerate discrimination in any form on the basis of race, color, religion, gender, age, national origin, citizenship, disability, veteran status or any other classification protected by federal, state or local law. All your information will be kept confidential.
Personal data collected during the recruitment process will be processed in accordance with our Recruiting Privacy Notice, which explains how your information is collected, used, and handled in connection with hiring activities. By applying for this position, you consent to this processing.
By submitting your application, you confirm that the information provided, including any supporting documents, is complete and accurate to the best of your knowledge. Any misrepresentation, omission, or falsification may result in disqualification from consideration or, if discovered after employment begins, termination of employment.
Benefits mentioned
- Unlimited paid time off, 12 paid holidays including 4 global VeeaMe Days for self-care and 24 paid volunteer hours annually through Veeam Cares
- Paid parental leave: 8 weeks for all parents, 16 weeks for birthing parents
- Medical, dental, and vision coverage starting on your first day
- Mental health support, therapy sessions, and digital wellness tools via our Employee Assistance Program
- 401(k) retirement plan with company matching contributions
- Fertility, adoption, and surrogacy support through Maven, plus paid volunteer time
- AirVet: 24/7 virtual veterinary care at no cost
- Legal services, identity protection, and supplemental health insurance options
- Tax-advantaged spending accounts for healthcare, dependent care, and commuting
- Opportunities to learn and grow through on-demand libraries (LinkedIn Learning, O’Reilly), mentoring, workshops, and learning events like our annual Global Day of Learning
Requirements mentioned
- 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, openCypher, SPARQL)
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