[Remote] Principal Security Data Engineer, Infrastructure Security Engineering - DGX Cloud
Note: The job is a remote job and is open to candidates in USA. NVIDIA is a leader in AI and high-performance computing, seeking a Principal Security Data Engineer to enhance their Infrastructure Security Engineering team. The role involves building the data backbone for their security control plane, creating pipelines, lakes, and analytics to ensure a trustworthy security state across a large GPU fleet.
Responsibilities
- Design, build, and operate the ingestion and transformation pipelines that collect security telemetry and asset inventory from dozens of heterogeneous sources, and normalize them into one canonical model
- Architect and run the storage layer. A data lake/lakehouse built on open formats, with the schema flexibility to absorb structured inventory, semi-structured telemetry, and unstructured logs without constant, breaking migrations
- Build the query and analytics layer that powers posture scoring, coverage and drift metrics, freshness monitoring, and multi-source correlation
- Treat the data platform as a high-value target, because it is. The data you store is a map of every host, every gap, and every credential path. You will engineer encryption at rest and in transit, fine-grained RBAC/ABAC, non-repudiable audit logging, data classification, network isolation, and verifiable retention and purge
- Build for stable identity, source attribution, append-only history, and honest coverage. Make a source going quiet a finding, not silence, so that every downstream number comes with a known confidence
- Partner with the security control plane team, the inventory systems, identity and endpoint teams, and broader NVIDIA data and security organizations to define data contracts early, so these systems converge by design
Skills
- Data Engineering at Scale: 15+ years of experience designing, building, and operating production data pipelines, lakes, or lakehouses at high volume and throughput. You build systemic solutions rather than performing manual data wrangling or 'tool administration.' Bachelor's degree or equivalent
- Production-Grade Coding: A strong software engineering background with the ability to write clean, maintainable, and well-tested code (e.g., Python, Go, Scala, SQL). You should be comfortable building and operating production data services at scale
- Data Modeling & Schema Design: Proven ability to design canonical schemas and data models that span many disparate sources and evolve over time without breaking the consumers that depend on them
- Distributed Data Systems: Hands-on experience with the modern data stacks, both streaming and batch processing, object storage, open table formats, and interactive query engines
- Security-Minded Data Handling: You design data systems that are themselves defensible. Access control, encryption, audit, and isolation are first-class concerns in your work, and you understand that security data is among the most sensitive data an organization holds
- Analytics Enablement: A track record of making large, messy datasets genuinely useful—serving interactive analysts, dashboards, and downstream services with data they can trust and query at low latency
- Foundation: Bachelor's degree in Computer Science, Engineering, or a related technical field (or equivalent experience)
- Security Telemetry & Detection Engineering: Experience building SIEM or data-lake detection content, normalizing security logs into common schemas (e.g., OCSF, ECS), or engineering the data layer that feeds correlation and anomaly-detection systems
- Real-Time & Streaming Data: Expertise building low-latency, near-real-time pipelines where a correlation is only as fast as its slowest input, and detection is measured in minutes
- HPC/AI Fleet Telemetry: Experience working with GPU and hardware telemetry (DCGM, Redfish/BMC, InfiniBand) or fleet-scale observability across hundreds of thousands of devices
- AI-Ready Data: Experience engineering the data and feature layers that feed ML or LLM-based reasoning systems, enabling agents to correlate, predict, and act on trustworthy data. How have you made data safe to reason over?
Benefits
- You will also be eligible for equity and benefits.
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