Used Tools & Technologies
Not specified
Required Skills & Competences
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Security @ 4
Kafka @ 4
Spark @ 4
Flink @ 4
Data Engineering @ 4
API @ 4
Databricks @ 4
Splunk @ 4
Snowflake @ 4
AI @ 4
Data Pipelines @ 7
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
At NVIDIA, we are redefining the future of computing. From accelerated AI to large scale infrastructure, the security of our platforms and data is critical to everything we build. On the Security Platform Engineering team, we focus on building the data foundations that power detection, automation, and AI driven security operations across the company.
How does raw security telemetry become usable intelligence? We build the platform that makes it possible. This role helps design and operate the security data platform that transforms signals from endpoint, identity, network, and cloud environments into standardized, high quality datasets. These datasets power the work of security engineers and researchers across NVIDIA. The role sits at the intersection of security engineering, data engineering, and platform architecture, enabling advanced detection, automation, and analytics across the company.
Responsibilities
- Design and operate telemetry ingestion pipelines that collect and process data from endpoint, identity, network, cloud, and other enterprise security sources.
- Normalize and enrich telemetry into structured datasets using standardized schemas and entity models so signals from different systems can be correlated consistently.
- Build and maintain data models and graph-ready structures that connect users, devices, identities, and activity across the security ecosystem.
- Provide governed access to security datasets through APIs, query interfaces, and streaming pipelines used by Detection, Automation, AI, and Analytics teams.
- Define lifecycle and retention strategies across hot, cold, and archive storage tiers to balance performance, scalability, and cost.
- Work closely with enterprise data engineering and security engineering teams to align on architecture, data fabric strategy, and shared platform capabilities.
- Maintain clear documentation of data sources, schemas, and entity definitions so teams across NVIDIA can reliably build on the platform.
Requirements
- Bachelor’s degree in Computer Science, Engineering, Cybersecurity, Data Engineering, or a related technical field, or equivalent experience.
- 6+ years of experience designing and operating large scale data pipelines in a security or enterprise data environment.
- Strong understanding of security telemetry including endpoint, identity, network, cloud, and email data sources.
- Experience working with modern data platforms and ingestion technologies such as Databricks, Snowflake, Kafka, Spark, Flink, or similar systems.
- Hands-on experience with data normalization frameworks or standards such as OCSF, ECS, or equivalent approaches.
- Understanding of data access patterns including APIs, query layers, views, and role based access control.
- Ability to collaborate across teams and clearly document complex data systems for a broad technical audience.
Ways to stand out from the crowd
- Experience working with security platforms such as CrowdStrike NG SIEM, Splunk, or Microsoft Sentinel.
- Familiarity with SIEM data models, detection engineering workflows, and SOAR integrations.
- Experience with graph databases or entity relationship modeling for security data.
Compensation & Benefits
- Base salary range for Level 4: 168,000 USD - 270,250 USD.
- Base salary range for Level 5: 196,000 USD - 310,500 USD.
- You will also be eligible for equity and benefits (links provided in the original posting).
Applications for this job will be accepted at least until June 27, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and is an equal opportunity employer. The company does not discriminate on the basis of protected characteristics as described in the original posting.