NVIDIA is hiring a Senior Data Engineer to join its Hardware Infrastructure organization and develop the foundational infrastructure for the Data & Observability Platform. You will build high-throughput pipelines moving petabytes of telemetry data, manage the central Data Lakehouse, and work embedded with engineering teams to optimize data schemas and efficiency.

What You'll Do

Build and deploy high-throughput, reliable pipelines to move substantial volumes of telemetry information from global edge locations to the central Data Lakehouse.
Lead the implementation of the tiered storage strategy and design efficient schemas optimized for write-heavy real-time ingestion and fast, cost-effective interactive queries.
Modernize workflow scheduling by implementing robust, code-based data pipelines that handle complex dependencies, automated backfills, and intelligent retries.
Partner directly with internal engineering teams to audit data usage, identify heavy-hitter datasets and primary storage consumers, refactor inefficient schemas, and enforce lifecycle policies to reduce storage costs.
Own the operation of the underlying platform, managing stateful deployments on Kubernetes, optimizing Spark performance, and ensuring the reliability of the streaming architecture.
Implement automated schema validation and data quality checks to prevent bad data from entering the lake and collaborate with security teams to apply automated masking and access controls.

What We're Looking For

BS or MS in Computer Science, Electrical Engineering, or a related field (or equivalent experience).
8+ years of experience in Data Engineering with a strong focus on Infrastructure, Streaming, or Platform building.
Expert proficiency in Python for automation, tooling, and orchestration. Proficiency in Java or Scala for high-performance data processing (Spark/Flink).
Extensive experience with Kafka, including deep understanding of consumer groups, partition strategies, offset management, and handling backpressure in high-volume environments.
Hands-on experience with modern table formats (Apache Iceberg, Delta Lake, or Hudi) and distributed query engines (Trino/Presto/Spark).
Experience deploying, configuring, and debugging applications on Kubernetes using Helm.

Nice to Have

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.

Technical Stack

Languages: Python, Java, Scala
Processing Frameworks: Apache Spark, Flink
Streaming: Kafka
Table Formats: Apache Iceberg, Delta Lake, Hudi
Query Engines: Trino, Presto
Infrastructure: Kubernetes, Helm, S3
Tooling: Vector

Team & Environment

This role is part of NVIDIA's Hardware Infrastructure organization, specifically the Data & Observability Platform. You will serve and collaborate directly with NVIDIA's AI, hardware, and software engineering and research teams.

Benefits & Compensation

Compensation: $184,000 USD - $287,500 USD for Level 4, and $224,000 USD - $356,500 USD for Level 5.
Equity grant eligibility.
Comprehensive benefits package (details via NVIDIA’s company benefits page).

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 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.