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Data Storage Jobs in California (NOW HIRING)

Innowhyte Inc

Optimize and manage data storage systems and ensure high availability, reliability, and performance. Design, develop, and maintain robust and scalable ETL (Extract, Transform, Load) and ELT (Extract ...

TSMC

Senior Data Engineer / Data Curator

San Jose, CA · On-site

$124K - $168K/yr

... storage and retrieval strategies, ensuring scalability and data consistency across different environments. • Conduct regular audits to ensure data integrity, privacy, and security compliance.

Innowhyte Inc

... data storage systems and ensure high availability, reliability, and performance. • Design, develop, and maintain robust and scalable ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform ...

OpenAI

Data Engineer, CPU & Storage

San Francisco, CA · On-site

$140 - $210/hr

CPU and storage data may originate from internal infrastructure platforms, vendor APIs, databases, object storage, capacity systems, and operational services. You will determine how to reliably ...

Jobsbridge

Big Data Engineer

Los Angeles, CA

$60 - $79.50/hr

... storage Build tools for proper data ingestion from multiple heterogeneous sources Requirements: 2+ years of experience in design and implementation in an environment with hundreds of terabytes of ...

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All JobsData Storage JobsData Storage Jobs in California

Data Storage information

See California salary details

$14

$36

$107

How much do data storage jobs pay per hour?

As of Aug 16, 2026, the average hourly pay for data storage in California is $36.94, according to ZipRecruiter salary data. Most workers in this role earn between $16.35 and $39.86 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a data storage specialist, and why are they important?

To thrive as a Data Storage Specialist, you need expertise in data management, storage architectures, and backup/recovery solutions, often supported by a degree in computer science or information technology. Familiarity with storage area networks (SAN), network-attached storage (NAS), cloud storage platforms, and certifications like CompTIA Storage+ or vendor-specific credentials are typically required. Strong problem-solving skills, attention to detail, and effective communication help you manage complex systems and collaborate across IT teams. These skills ensure data integrity, security, and availability, which are critical for organizational operations and disaster recovery.

What is the difference between Data Storage vs Data Analyst?

AspectData StorageData Analyst
Required CredentialsKnowledge of database systems, certifications like CompTIA Storage+Degree in statistics, data science, or related fields; certifications like Microsoft Data Analyst
Work EnvironmentData centers, IT departments, cloud storage facilitiesOffice settings, analytics teams, business departments
Employer & Industry UsageIT companies, cloud providers, data centersBusiness, finance, marketing, and healthcare sectors
Common Search & Comparison IntentUnderstanding storage solutions, infrastructure rolesAnalyzing data, generating insights

Data Storage focuses on managing and maintaining data infrastructure, while Data Analysts interpret data to support decision-making. Both roles are essential in data-driven organizations but serve different functions within the data ecosystem.

What is data storage and why is it important?

Data storage refers to the process of saving digital information on various types of storage media, such as hard drives, solid-state drives, cloud platforms, or optical discs. It is essential for businesses and individuals to securely store, manage, and access their data when needed. Effective data storage ensures data protection, supports business continuity, and enables efficient information retrieval for operations and decision-making.

What are the typical challenges faced when managing large-scale data storage systems, and how are they addressed within a team setting?

Professionals in data storage roles often encounter challenges such as ensuring data security, minimizing downtime, and optimizing performance as storage needs grow. Addressing these issues typically involves close collaboration with IT, network, and security teams to implement robust backup solutions, monitor system health, and maintain compliance with data regulations. Regular team meetings and cross-functional projects are common, allowing team members to share best practices and quickly respond to incidents, ensuring the reliability and scalability of storage systems.

What are the most commonly searched types of Data Storage jobs in California?

The most popular types of Data Storage jobs in California are:

What are popular job titles related to Data Storage jobs in California?

For Data Storage jobs in California, the most frequently searched job titles are:

What job categories do people searching Data Storage jobs in California look for?

The top searched job categories for Data Storage jobs in California are:

What cities in California are hiring for Data Storage jobs?

Cities in California with the most Data Storage job openings:

Data Storage jobs near you
Infographic showing various Data Storage job openings in California as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $76,837 per year, or $36.9 per hour.

Distinguished Engineer, Storage - AI Cloud

Nvidia Corporation

Santa Clara, CA • On-site

Full-time

Re-posted 2 days ago

Nvidia rating

9.6

Company rating: 9.6 out of 10

Based on 17 frontline employees who took The Breakroom Quiz

7th of 244 rated software companies

Job description

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology-and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent. As an NVIDIAN, you'll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world.
AI Cloud Data Storage
NVIDIA DGXC Storage org handles some of the fastest training and inference tasks. Every GPU cycle depends on a storage platform built to keep tens of thousands of accelerators continuously busy. It maintains exabytes of data securely and powers the largest AI workloads worldwide across cloud, neocloud, and on-prem setups. With the growth of accelerated computing, storage is essential. It can make the difference between effective GPU use and wasted potential and between launching a frontier model on time or missing the deadline by months. We seek a Distinguished Engineer to lead NVIDIA's storage strategy for AI Cloud across the Neocloud Provider (NCP) and Cloud Service Provider (CSP) ecosystem. You will direct the architecture of high-performance parallel file systems, object stores, and block storage at exabyte scale. You will stay hands-on, collaborating with engineers, SREs, partners, and storage vendors. You will apply NVIDIA's AI tools to increase your productivity and that of those you impact. This is a distinctive prospect to establish the storage framework of the AI era at the company that introduced accelerated computing.
What you'll be doing:
  • Lead the multi-year technical plan for AI Cloud Storage expansion across NCPs - determine the reference architecture, capabilities, performance and durability SLOs, qualification methodology, and roadmap for the high-performance file, object, and block storage that each NCP must offer to qualify for NVIDIA GPU allocation.
  • Serve as the chief storage architect with deep hands-on involvement. Lead key reviews of storage builds and investigate root causes of complex production problems. Develop prototype reference implementations to minimize risks in new initiatives. Make final technical decisions on NCP storage deliveries using measurable SLOs. Apply AI tools heavily to amplify your technical influence throughout the program.
  • Define the standard for "production-ready" in NCP storage, including durability and availability SLOs measured in 9s. Ensure sustained efficiency per TiB, observability, blast-radius containment, and reduced operational toil. Influence GPU delivery gating by requiring AI Cloud to accept GPU capacity only after verifying storage-focused ancillary services.
  • Develop and guide the architectural direction by working closely with collaborators in training, inference, and accelerated-computing product lines. Coordinate with site-reliability, operations, networking, and security colleagues. Work together with external cloud providers, neocloud operators, and storage vendors to align on a common architecture.
  • Develop the open-source path forward for AI storage. Establish and guide an open-source strategy that broadens the AI storage ecosystem. Advocate for a GitHub-first, security-first stance. Engage deeply with upstream open-source communities. Formalize the APIs, SDKs, and protocols allowing partners and the industry to build, integrate, and create with NVIDIA at the AI storage level.
  • Lead an engineering culture centered on AI tools. Regularly use modern AI coding and agentic tools in your daily tasks. Show what 10× engineering means at NVIDIA. Distribute patterns, prompts, and evaluation harnesses across the storage organization.
  • Partner with peer Distinguished and Principal storage architects across the organization to tackle the most difficult, long-term technical challenges. Make automation the only acceptable solution for infrastructure management tasks like live software upgrades, node and drive replacements, capacity rebalancing, cross-DC data movement, and dataset lifecycle. Establish root-cause analysis and corrective action rigor on every major incident. Design the storage layer for workloads spanning the next several GPU generations, including disaggregated inference with storage-backed KV caching, large-scale write-once-read-many inference patterns, exabyte regional object stores, and cross-DC dataset versioning and copy management.
  • Mentor and develop senior, principal, and distinguished engineers across the storage organization and nearby business units. Raise the technical bar broadly. Represent NVIDIA externally in standards bodies, open-source communities, customer briefings, and industry forums (FAST, SC, OCP, SNIA, Linux Storage Summit).

What we need to see:
  • BS, MS, or PhD in Computer Science, Electrical Engineering, or a related field - or equivalent experience.
  • A minimum of 18+ years of practical engineering experience in storage technology is needed. This involves extensive involvement with a high-performance parallel file system like Lustre, GPFS / Spectrum Scale, WEKA, VAST, BeeGFS, DAOS, or its equivalent, handling data at multi-petabyte scale. Candidates must also have wide-ranging expertise in object storage (S3 / Swift-class) and block storage (NVMe-oF, NVMesh-class, iSCSI).
  • A track record of crafting and managing storage platforms at exabyte scale for performance-critical workloads - AI training, HPC, video, or hyperscale data lakes - including direct responsibility for durability, availability, and performance SLOs measured in 9s.
  • Demonstrated ability to set technical strategy across business units and partner organizations. You have driven multi-year storage architectures adopted by multiple teams, vendors, or customers. You can point to measurable outcomes such as GPU utility lift, $/PB reduction, incidents eliminated, and time-to-bring-up compressed.
  • You are 100% hands-on in engineering. You write and review production code yourself. When a bug requires it, you read Lustre, NFS, kernel, NVMe-oF, or SPDK source code. You also run scale tests or recovery drills personally instead of delegating.
  • Strong proficiency in at least one systems language (C, C++, Rust, or Go) and proficiency in Python; comfortable in the Linux kernel storage and networking stacks (block layer, RDMA / RoCE / InfiniBand, NVMe, page cache, VFS, multipath).
  • Frequent daily use of advanced AI coding and autonomous tools, including specific examples showing how you accelerated building, coding, debugging, validation, and operations. Also, share your perspective on future trends.
  • Excellent written and verbal communication. You can write a one-pager that aligns a VP. You can also write a six-pager that aligns an entire org. You can explain a deep technical trade-off to an SRE, a vendor CTO, and an internal customer in the same week.
  • Comfort operating in a 24/7 production environment where storage incidents directly impact GPU revenue, with a security-first approach baked into every build.

Ways to stand out from the crowd:
  • Proven background in designing or managing storage solutions for AI training or inference at 10k+ GPU scale, demonstrating clear improvements in GPU utilization or reducing I/O bottlenecks.
  • Open-source contributions or maintainership in Lustre, NFS, SPDK, NVMe / NVMe-oF, CSI, Ceph, MinIO, RocksDB, or related projects.
  • Built or led a disaggregated-inference or Inference-Time-Compute storage architecture - KV caching to fast in-cluster or GPU-adjacent storage, WORM at scale, storage-aware scheduling, or database-integrated inference.
  • Public technical contributions - patents, peer-reviewed papers (FAST, SOSP, NSDI, OSDI, ATC), keynote talks, or RFCs - that demonstrate expertise and leadership in storage for AI infrastructure.

NVIDIA led the way in accelerated computing. Today, our AI infrastructure drives global intelligence, changing industries worldwide. The AI Cloud Storage group forms the base that maintains the world's largest GPU fleet's productivity. Every model trained, every inference served, and every checkpoint saved passes through systems we develop, construct, and manage.
Widely considered to be one of the technology world's most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family www.nvidiabenefits.com/
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 320,000 USD - 488,750 USD.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until August 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.

What Nvidia employees say

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Hours and flexibility

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About Nvidia

Sourced by ZipRecruiter

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology--and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Santa Clara, CA, US

Year founded

1993

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