Hybrid Senior System Software Engineer – Data Engineering

at NVIDIA

• Job title: Senior System Software Engineer – Data Engineering
• Job type: Full Time
• Experience level: Senior
• Salary: $184,000 - $356,500 per year
• Degree requirement: Bachelor's Degree
• Tech skills: Apache, Java, Kafka, Kubernetes, Python, Scala, Spark
• Location requirements: Hybrid, Santa Clara, United States

About the role

• Infrastructure-Focused Data Engineer for NVIDIA’s Data & Observability Platform. Developing data pipelines and managing Data Lakehouse for massive-scale operations.

Responsibilities

• 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.

Requirements

• 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.
• 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.

Benefits

• Equity and benefits