Senior Staff Software Engineer, Data Engineering

Nvidia

Confirmed live 2 days ago High trust

Quick summary

Work type
On-site
Location
Santa Clara, CA
Salary
$200,000–$322,000 / yr
Employment
Full-time
Posted
4 days ago
Freshness
Confirmed live 2 days ago

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $190k
This role $261k
$122k $343k
below market most similar roles pay here above market

This role pays more than 94% of similar roles. Most pay $159,318–$221,375 — the blue band above. At the midpoint, this role pays about $261k versus about $190k for comparable roles.

Based on 240 similar postings.

Employer

About Nvidia

Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing

Nvidia currently has 1626 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 1115 roles with salary data.

Most-posted roles

View all roles at Nvidia

At a glance

TL;DR · Senior Staff Software Engineer, Data Engineering

Senior Staff Software Engineer - Data Engineering joins the FinOps team to build and operate production pipelines and data models for cost allocation, forecasting, and optimization. The role involves designing reusable ingestion frameworks and data contracts for cloud billing, SaaS usage, and shared-service telemetry. Key responsibilities include developing cost and ownership models, implementing data freshness and accuracy checks, and optimizing distributed processing for performance and scalability. The candidate will use Python, SQL, Databricks, and Apache Spark to manage lakehouse technologies, REST APIs, and CI/CD workflows. This position requires solving complex data transformation problems, handling schema evolution, and ensuring production reliability through automated testing and monitoring. The work focuses on turning reporting and governance requirements into clear data models while ensuring auditability throughout the data lifecycle.

What does a Software Engineer earn in California?

Median $214500 from 1223 postings across 76 companies.

See salary data

What you'll do

  • Design, build, and operate production pipelines using Python, SQL, and Databricks/Spark for cloud billing and telemetry.
  • Build reusable ingestion frameworks and data contracts to handle schema changes, late-arriving data, and safe backfills.
  • Develop cost, usage, and ownership models for business unit and platform allocation with full traceability.
  • Implement data freshness, completeness, and accuracy checks along with lineage and actionable monitoring.
  • Optimize distributed processing, storage, and serving-layer queries for performance and infrastructure cost.
  • Own testing, CI/CD, safe deployments, and operational runbooks for data services while providing production support.
  • Translate reporting and governance requirements from Finance and infrastructure partners into clear data models and interfaces.
  • Lead technical design and code reviews while mentoring engineers and applying access controls throughout the data lifecycle.

What we're looking for

  • 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 with experience 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.
  • Experience with Git, CI/CD, and secure handling of credentials and data access.
  • Familiarity with FinOps, cloud billing datasets, FOCUS, SaaS license utilization, or cost-allocation models (preferred).

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