Data Analytics Engineer Jobs at NVIDIA with Visa Sponsorship
Data Analytics Engineer jobs at NVIDIA sit at the intersection of large-scale data infrastructure and product intelligence, supporting GPU computing, AI platforms, and enterprise solutions. NVIDIA has a consistent track record of sponsoring international talent in technical engineering functions, making it a realistic target for H-1B visa and E-3 visa applicants.
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Get Access To All JobsINTRODUCTION
NVIDIA is seeking a software-focused Senior Failure Analysis Engineer who can blend deep development with production support ownership. This hybrid role sits at the intersection of software engineering, data infrastructure, semiconductor development, and production tooling — building and sustaining the intelligent platforms and workflows that power failure analysis, debug, and engineering insight at scale. You'll own the reliability and continuous improvement of production-critical FA systems (databases, CAD navigation tools, and analysis platforms) while partnering with failure analysis, design, verification, CAD, infrastructure, and manufacturing teams. This is a high-impact opportunity for someone who thrives on both building robust software and ensuring the tools that semiconductor teams depend on are always fast, reliable, and insightful.
ROLE AND RESPONSIBILITIES
- Own the reliability, performance, and continuous improvement of production-critical systems, including databases, CAD navigation tools, and failure analysis platforms, ensuring high availability and responsiveness for semiconductor engineering and manufacturing teams.
- Design and deliver scalable automation frameworks, data pipelines, and intelligent workflows that streamline semiconductor engineering, failure analysis, and production support processes at scale.
- Build advanced analytics platforms, dashboards, and orchestration systems that turn engineering and production data into clear, actionable insight for faster debug and better decision-making.
- Apply AI, machine learning, and optimization techniques to reduce manual effort, accelerate root-cause analysis, and strengthen both engineering and production workflows.
- Partner closely with failure analysis, design, verification, CAD, infrastructure, and production collaborators to deliver reliable, maintainable, and high-impact technical solutions.
- Drive continuous improvement in software quality, usability, performance, and operational excellence across large-scale compute, data, and production environments.
BASIC QUALIFICATIONS
- BS or MS in Electrical Engineering, Computer Engineering, Computer Science, or a related technical field, or equivalent experience.
- 8+ years of professional experience in software engineering, electrical engineering, or semiconductor development/production environments.
- Strong proficiency in Python, Rust, Shell scripting, or similar languages for building robust automation, tooling, and production systems.
- Proven track record designing automation frameworks, data-processing systems, or productivity tools with measurable engineering or production impact.
- Solid experience in Linux environments and modern software engineering guidelines (version control, testing, CI/CD, observability).
- Exceptional analytical and problem-solving skills with success navigating complex, multidisciplinary technical and production challenges.
- Strong collaboration and communication skills with proven efficiency across multi-functional engineering and production teams.
PREFERRED QUALIFICATIONS
- Direct experience in semiconductor design, silicon development, failure analysis, yield engineering, or engineering automation and production support workflows.
- Hands-on application of AI/ML, data analytics, or optimization methods to technical, hardware, or production-related problems.
- Familiarity with EDA workflows, design infrastructure, CAD navigation systems, or semiconductor tooling and lab/production environments.
- Track record architecting and operating scalable data pipelines, analytics platforms, or workflow orchestration systems in production settings.
- Proven ability to independently scope, drive, and deliver technical projects end-to-end while balancing development and production support responsibilities in fast-paced environments.
COMPENSATION
- Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 144,000 USD - 230,000 USD. You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until June 16, 2026. This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive 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 (including in our hiring and promotion practices) 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.
JR2019318
INTRODUCTION
NVIDIA is seeking a software-focused Senior Failure Analysis Engineer who can blend deep development with production support ownership. This hybrid role sits at the intersection of software engineering, data infrastructure, semiconductor development, and production tooling — building and sustaining the intelligent platforms and workflows that power failure analysis, debug, and engineering insight at scale. You'll own the reliability and continuous improvement of production-critical FA systems (databases, CAD navigation tools, and analysis platforms) while partnering with failure analysis, design, verification, CAD, infrastructure, and manufacturing teams. This is a high-impact opportunity for someone who thrives on both building robust software and ensuring the tools that semiconductor teams depend on are always fast, reliable, and insightful.
ROLE AND RESPONSIBILITIES
- Own the reliability, performance, and continuous improvement of production-critical systems, including databases, CAD navigation tools, and failure analysis platforms, ensuring high availability and responsiveness for semiconductor engineering and manufacturing teams.
- Design and deliver scalable automation frameworks, data pipelines, and intelligent workflows that streamline semiconductor engineering, failure analysis, and production support processes at scale.
- Build advanced analytics platforms, dashboards, and orchestration systems that turn engineering and production data into clear, actionable insight for faster debug and better decision-making.
- Apply AI, machine learning, and optimization techniques to reduce manual effort, accelerate root-cause analysis, and strengthen both engineering and production workflows.
- Partner closely with failure analysis, design, verification, CAD, infrastructure, and production collaborators to deliver reliable, maintainable, and high-impact technical solutions.
- Drive continuous improvement in software quality, usability, performance, and operational excellence across large-scale compute, data, and production environments.
BASIC QUALIFICATIONS
- BS or MS in Electrical Engineering, Computer Engineering, Computer Science, or a related technical field, or equivalent experience.
- 8+ years of professional experience in software engineering, electrical engineering, or semiconductor development/production environments.
- Strong proficiency in Python, Rust, Shell scripting, or similar languages for building robust automation, tooling, and production systems.
- Proven track record designing automation frameworks, data-processing systems, or productivity tools with measurable engineering or production impact.
- Solid experience in Linux environments and modern software engineering guidelines (version control, testing, CI/CD, observability).
- Exceptional analytical and problem-solving skills with success navigating complex, multidisciplinary technical and production challenges.
- Strong collaboration and communication skills with proven efficiency across multi-functional engineering and production teams.
PREFERRED QUALIFICATIONS
- Direct experience in semiconductor design, silicon development, failure analysis, yield engineering, or engineering automation and production support workflows.
- Hands-on application of AI/ML, data analytics, or optimization methods to technical, hardware, or production-related problems.
- Familiarity with EDA workflows, design infrastructure, CAD navigation systems, or semiconductor tooling and lab/production environments.
- Track record architecting and operating scalable data pipelines, analytics platforms, or workflow orchestration systems in production settings.
- Proven ability to independently scope, drive, and deliver technical projects end-to-end while balancing development and production support responsibilities in fast-paced environments.
COMPENSATION
- Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 144,000 USD - 230,000 USD. You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until June 16, 2026. This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive 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 (including in our hiring and promotion practices) 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.
JR2019318
See all Data Analytics Engineer Jobs at NVIDIA
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Data Analytics Engineer Jobs at NVIDIA.
Get Access To All JobsTips for Finding Data Analytics Engineer Jobs at NVIDIA
Align Your Portfolio to NVIDIA's Data Stack
NVIDIA's Data Analytics Engineering roles frequently involve pipelines built on Spark, dbt, and cloud-native warehouses like Snowflake or BigQuery. Frame your portfolio projects around GPU-accelerated analytics or AI-adjacent data workflows to signal direct relevance before your application lands with a recruiter.
Target Roles Listed Under Hardware and Platform Divisions
NVIDIA posts Data Analytics Engineer openings across multiple internal divisions. Roles tied to GeForce, CUDA, or enterprise AI platforms tend to have longer hiring cycles but stronger sponsorship intent, since they sit within established headcount plans rather than exploratory team builds.
Confirm Specialty Occupation Standing for Your Degree
H-1B eligibility requires your role to qualify as a specialty occupation under USCIS standards, meaning a specific bachelor's degree field is normally the minimum entry requirement. If your degree is in a tangential field like applied mathematics or statistics, gather documentation showing direct relevance to the Data Analytics Engineer job description before the offer stage.
Negotiate Offer Timing Around the H-1B Cap Calendar
USCIS opens H-1B registration each March for an October 1 start date. If you receive an offer outside that window, ask NVIDIA's immigration team early whether a cap-exempt pathway or a change-of-status bridge on a current visa applies to your situation.
Use Migrate Mate to Filter Open Roles by Visa Type
NVIDIA maintains a large number of active Data Analytics Engineer openings at any given time. Use Migrate Mate to filter specifically for positions tagged to H-1B or E-3 sponsorship, so you're only investing preparation time in roles where your visa pathway is already supported.
Prepare for a Technical Loop Before Immigration Discussions Begin
NVIDIA's interview process for analytics engineering roles typically includes SQL and data modeling assessments alongside system design questions. Immigration and sponsorship conversations happen after the technical loop clears, so demonstrate technical depth first rather than raising visa logistics in early recruiter screens.
Frequently Asked Questions
Does NVIDIA sponsor H-1B visas for Data Analytics Engineers?
Yes, NVIDIA sponsors H-1B visas for Data Analytics Engineer roles. The company works with immigration counsel to file petitions through the standard USCIS cap process each spring, with an October 1 employment start date. If you're already on a valid status like F-1 OPT or L-1 visa, NVIDIA can also support a change of status rather than requiring consular processing.
How do I apply for Data Analytics Engineer jobs at NVIDIA?
Applications go through NVIDIA's careers portal at nvidia.com/en-us/about-nvidia/careers. Search for Data Analytics Engineer and filter by location or team. Tailor your resume to reflect data pipeline experience, analytical tooling relevant to NVIDIA's stack, and any GPU or AI-adjacent project work. You can also browse verified sponsorship-tagged openings on Migrate Mate to identify which postings actively support international candidates.
Which visa types does NVIDIA commonly use for Data Analytics Engineer roles?
NVIDIA sponsors H-1B visas most frequently for Data Analytics Engineers, which covers the broadest pool of international applicants. Australian citizens are eligible for the E-3 visa, which bypasses the H-1B lottery and follows a similar specialty occupation standard. For longer-term pathways, NVIDIA also supports Green Card sponsorship through EB-2 and EB-3 classifications, typically initiated after an employee has been with the company for some time.
What qualifications and experience does NVIDIA expect for Data Analytics Engineers?
NVIDIA typically expects a bachelor's degree or higher in computer science, data engineering, statistics, or a closely related field. Hands-on experience with SQL, Python, and at least one cloud data warehouse platform is standard. Roles that sit closer to the AI infrastructure side of the business often expect familiarity with large-scale data pipelines, ETL orchestration tools like Airflow, and some exposure to GPU computing environments or ML data workflows.
How do I navigate the timeline between offer and visa filing at NVIDIA?
The critical window is USCIS's H-1B registration period, which opens in early March each year. NVIDIA's immigration team needs to register your petition before that deadline for an October 1 start. If you receive your offer after registration closes, discuss bridge options with your recruiter, including whether your current F-1 OPT or another status allows you to begin work while the next cap cycle opens.