Senior Staff Software Engineer - Data Engineering

NVIDIA•Santa Clara, CA

2d•$200,000 - $322,000

About The Position

NVIDIA is hiring a Senior Staff Software Engineer focused on data engineering to join its FinOps team. The role builds and operates the pipelines and data models behind cost allocation, forecasting, and optimization, improving data reliability and enabling new data sources to be onboarded with less manual effort. The engineer will develop production data pipelines and reusable ingestion frameworks, create cost and ownership models, implement data quality and monitoring capabilities, and optimize distributed processing and queries. The position owns testing, CI/CD, safe deployments, and operational runbooks, and participates in production support. It also partners with Finance, infrastructure, and service owners to translate reporting and governance needs into data models and interfaces, while leading technical design and code reviews and mentoring engineers. The base salary range is USD 200,000–322,000, determined based on location, experience, and pay of employees in similar positions. The role is also eligible for equity and benefits. Applications will be accepted at least until October 27, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes.

Requirements

Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or a related field, or equivalent experience.
12+ years of software or data engineering experience building and operating production data systems.
Strong Python and SQL skills, including writing maintainable, tested code and debugging complex data transformations.
Hands-on experience with Databricks, Apache Spark, and lakehouse technologies, or comparable distributed data platforms.
Experience with data modeling, incremental processing, partitioning, schema evolution, and analytical query performance.
Experience building cloud-based pipelines and integrating REST APIs, including authentication, pagination, rate limits, and error handling.
Track record of owning production reliability through automated tests, data quality checks, monitoring, incident diagnosis, and recovery.
Clear technical communication and ability to resolve ambiguous data requirements with engineering and business partners.
Experience with Git, CI/CD, and secure handling of credentials and data access.
Sound judgment when using AI coding tools and validating generated code.

Nice To Haves

Familiarity with FinOps, cloud billing datasets, FOCUS, SaaS license utilization, or cost-allocation models across cloud and shared services.
Experience with data catalogs, workflow orchestration, observability platforms, or AI-assisted anomaly detection and root-cause analysis.

Responsibilities

Design, build, and operate production pipelines using Python, SQL, and Databricks/Spark for cloud billing, SaaS usage, and shared-service telemetry.
Build reusable ingestion frameworks and data contracts for API and file-based sources, handling schema changes, late-arriving data, and safe backfills.
Develop cost, usage, and ownership models for business-unit, platform, and service allocation, with reconciliation and traceability to source records.
Implement data freshness, completeness, and accuracy checks, lineage, and actionable monitoring; diagnose incidents and automate retries, replay, and recovery.
Optimize distributed processing, storage, and serving-layer queries for performance, scalability, and infrastructure cost.
Own testing, CI/CD, safe deployments, and operational runbooks for data services, and participate in production support.
Partner with Finance, infrastructure, and service owners to translate reporting and governance requirements into clear data models and interfaces.
Lead technical design and code reviews, mentor engineers, and apply access controls and auditability throughout the data lifecycle.