2 days ago
Base Salary
$200k - $322k/yr
Responsibilities
- Design, build, and operate production pipelines using Python, SQL, Databricks, and Apache Spark for cloud billing, SaaS usage, and shared-service telemetry.
- Build reusable ingestion frameworks and data contracts for API and file-based sources, including schema changes, late-arriving data, and safe backfills.
- Develop cost, usage, ownership, reconciliation, and traceability models for business units, platforms, and services.
- Implement data freshness, completeness, accuracy, lineage, monitoring, automated retries, replay, and recovery processes.
- Optimize distributed processing, storage, and serving-layer queries for performance, scalability, and infrastructure cost.
- Own testing, deployments, operational runbooks, production support, and data-service reliability.
- Partner with Finance, infrastructure, and service owners to translate reporting and governance requirements into data models and interfaces.
- Lead technical design and code reviews, mentor engineers, and apply access controls and auditability across the data lifecycle.
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 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 APIs with authentication, pagination, rate-limit, and error-handling considerations.
- Track record of production reliability ownership 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, secure credential handling, and data access controls.
- Familiarity with FinOps, cloud billing datasets, FOCUS, SaaS license utilization, or cost-allocation models is preferred.
- Experience with data catalogs, workflow orchestration, observability platforms, or AI-assisted anomaly detection and root-cause analysis is preferred.
Benefits
- Base salary range is 200,000 USD to 322,000 USD, with eligibility for equity and benefits.
- Applications will be accepted at least until October 27, 2026.
- This posting is for an existing vacancy.
Tech Stack
Categories
Data Engineering
About Nvidia
Nvidia designs and sells GPUs and accelerated computing platforms for data centers, AI/ML, graphics, gaming, and automotive, monetizing through hardware, software platforms (CUDA, AI frameworks), and systems like DGX and networking. Customers include cloud providers, enterprises, researchers, and OEMs. Founded in 1993 and headquartered in Santa Clara, it is a public company traded on NASDAQ under NVDA.