NVIDIA’s Hardware Infrastructure organization is looking for a Senior Data Engineer to become part of the Data & Observability Platform. We serve and collaborate directly with NVIDIA’s rapidly growing AI, HW, and SW engineering and research teams to provide the data backbone that powers our massive-scale operations. We are seeking an Infrastructure-Focused Data Engineer to develop the foundational infrastructure of our data platform. In this role, you will build high-throughput pipelines that move petabytes of telemetry data and manage our central Data Lakehouse. Uniquely, you will also work in an embedded capacity with engineering teams, optimizing their data schemas and efficiency to solve real-world scale challenges.
What you’ll be doing:
Build Scalable Data Pipelines: Develop and deploy high-throughput, reliable pipelines to move substantial volumes of telemetry information from global edge locations to our central Data Lakehouse.
Architect the Data Lakehouse: Lead the implementation of our tiered storage strategy. You will design efficient schemas that optimize for both write-heavy real-time ingestion and fast, cost-effective interactive queries.
Orchestration & Automation: Modernize workflow scheduling by implementing robust, code-based data pipelines. You will build workflows that handle complex dependencies, automated backfills, and intelligent retries.
Drive Embedded Data Optimization: Partner directly with internal engineering teams to audit their data usage. You will identify heavy-hitter datasets and primary storage consumers, refactor inefficient schemas, and enforce lifecycle policies to significantly reduce storage costs.
Manage Data Infrastructure: Own the operation of the underlying platform. You will manage stateful deployments on Kubernetes, optimize Spark performance, and ensure the reliability of our streaming architecture.
Enforce Quality & Governance: Implement automated schema validation and data quality checks to prevent bad data from entering the lake. You will collaborate with security teams to apply automated masking and access controls.
What we need to see:
BS or MS in Computer Science, Electrical Engineering, or related field (or equivalent experience).
8+ years of experience in Data Engineering with a strong focus on Infrastructure, Streaming, or Platform building.
Strong Coding Fluency: Expert proficiency in Python for automation, tooling, and orchestration. Proficiency in Java or Scala for high-performance data processing (Spark/Flink).
Deep Streaming Expertise: Extensive experience with Kafka. You have a deep understanding of consumer groups, partition strategies, offset management, and handling backpressure in high-volume environments.
Data Lake Experience: Hands-on experience with modern table formats (Apache Iceberg, Delta Lake, or Hudi) and distributed query engines (Trino/Presto/Spark).
Containerization & Ops: Deploy, configure, and debug applications on Kubernetes using Helm.
Ways to stand out from the crowd:
Familiarity with EDA workflows, semiconductor design lifecycles, or experience managing simulation/emulation logs for hardware engineering teams.
Ability to navigate complex organizational structures, partnering with hardware architects and engineering leads to translate broad requirements into concrete data infrastructure solutions.
Experience migrating from legacy search stores (Elasticsearch/OpenSearch) to Cold Storage (S3/Iceberg).
Experience with high-performance log routing frameworks like Vector.
Background in identifying cost drivers in petabyte-scale environments and implementing storage cost optimization initiatives.
You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
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NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
