Senior Engineer, DGX Cloud Resilience Engineering - Data Platform

NVIDIA•Santa Clara, MA

6d•$184,000 - $356,500•Hybrid

About The Position

NVIDIA is seeking a Senior Engineer for DGX Cloud Resilience Engineering – Data Platform. The team builds and operates the operational data foundation that supports reliability and resiliency across the DGX Cloud program. Its platforms, pipelines, and schemas help engineering and leadership teams measure, understand, and improve operational excellence at scale. The role owns the data platform end to end, including pipelines that ingest, transform, and serve operational data. It focuses on keeping pipelines reliable, observable, fresh, and high quality, and on using operational metrics to prioritize improvements. The engineer partners with customer teams to translate data needs into platform capabilities and schemas, while providing technical leadership through standards, design and code reviews, mentoring, and roadmap development.

Requirements

7+ years of experience in software technical leadership roles in an agile software development environment.
BS in Computer Science, Engineering, or a related field, or equivalent experience.
Confirmed experience owning and operating production data pipelines, with a track record of keeping them reliable, observable, and fresh.
Hands-on expertise in Databricks, including designing and leading schemas, tables, and access patterns using Delta Lake.
Strong Python skills and experience building maintainable, well-tested, production-grade data code.
Solid understanding of medallion architecture and data modeling practices, with judgment to balance data quality, performance, and usability.
Experience applying data lineage, retention, and access policies in production environments.

Nice To Haves

Experience building data products and tooling that enables agentic workflows and analysis.
Experience working with APIs for operational data, including PagerDuty, Rootly, and Backstage.

Responsibilities

Own and operate the data platform end to end, including pipelines that ingest, transform, and serve operational data to leadership and partner teams.
Set and meet targets for pipeline uptime, data freshness, and quality; lead incident response, root cause analysis, and prevention work.
Define, instrument, and report on operational success metrics, including SLIs/SLOs, pipeline latency, and data completeness, and use them to drive prioritization and continuous improvement.
Partner with customer teams to understand data needs, translate those needs into platform capabilities and schemas, and set expectations for support and delivery.
Set engineering standards, review designs and code, mentor engineers, and drive the platform roadmap.