Principal Security Data Engineer, Infrastructure Security Engineering - DGX Cloud

NVIDIA•Santa Clara, CA

1d•$272,000 - $431,250

About The Position

NVIDIA DGX Cloud is the AI supercomputing-as-a-service substrate designed to power the next generation of AI and industrial-scale breakthroughs. As a Security Data Engineer within our Infrastructure Security Engineering organization, you will build the data backbone of our security control plane—the pipelines, lake, and analytics that turn fragmented telemetry from a 250,000+ GPU fleet into a single, queryable, trustworthy picture of security state. Every posture score, every detection, and every autonomous action our platform takes stands on the data foundation you engineer.

Requirements

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.
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.
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.
Hands-on experience with the modern data stacks, both streaming and batch processing, object storage, open table formats, and interactive query engines.
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.
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.
Bachelor's degree in Computer Science, Engineering, or a related technical field (or equivalent experience).

Nice To Haves

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.
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.
Experience working with GPU and hardware telemetry (DCGM, Redfish/BMC, InfiniBand) or fleet-scale observability across hundreds of thousands of devices.
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?

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.