Senior Data Engineer, Ops Data Platform at NVIDIA

Santa Clara, California, United States

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Not SpecifiedCompensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Technology, AIIndustries

Requirements

Bachelor’s or master’s degree (or equivalent experience) in computer science or information system, or equivalent experience with programming knowledge (i.e., Python, Java, etc.)
8+ years of experience in data and database management that includes data pipeline responsibilities in replication and mass ingestion, streaming, API and application and data integration
Experience developing and maintaining data warehouses in big data solutions, including several ecosystems like Azure, AWS, and Hadoop environments
Built required infrastructure for optimal extraction, transformation, and loading of data from various sources using AWS, Azure, SQL or other technologies
Knowledgeable in Databricks, Splunk, or Snowflake solutions
Knowledge of data analytics/mining and segmentation techniques
Communicate effectively with business users and translate business needs into technology solutions
Ability to connect with all team members successfully and effectively
High energy with positive problem-solving attitude, multi-tasking, and strong organizational skills
Strong analytical skills with the ability to collect, organize, and disseminate significant amounts of information with attention to detail and accuracy
Knowledge in operational processes in semi-chips, boards, systems, and servers with a view of data landscape

Responsibilities

Implement End-to-End Quality Data platform solutions for Datacenter products
Build data pipelines that are used to transport data from a data source to the data lake
Analyze and organize raw data
Build data systems and pipelines ensuring that data sources, ingestion components, transformation functions, and destination are well understood for implementation
Interpret trends and patterns by performing complex data analysis
Prepare data for prescriptive and predictive modeling by making sure that the data is complete, has been cleansed, and has the necessary rules in place
Build algorithms and prototypes
Develop analytical tools and programs
Collaborate with data scientists and architects on projects
Evaluate business needs and objectives to ensure the organization can access the raw data

Skills

Data Pipelines

Data Lakes

Data Ingestion

Data Transformation

Data Cleansing

Complex Data Analysis

Predictive Modeling

Algorithms

Prototyping

Analytical Tools

ETL

NVIDIA

Designs GPUs and AI computing solutions

About NVIDIA

NVIDIA designs and manufactures graphics processing units (GPUs) and system on a chip units (SoCs) for various markets, including gaming, professional visualization, data centers, and automotive. Their products include GPUs tailored for gaming and professional use, as well as platforms for artificial intelligence (AI) and high-performance computing (HPC) that cater to developers, data scientists, and IT administrators. NVIDIA generates revenue through the sale of hardware, software solutions, and cloud-based services, such as NVIDIA CloudXR and NGC, which enhance experiences in AI, machine learning, and computer vision. What sets NVIDIA apart from competitors is its strong focus on research and development, allowing it to maintain a leadership position in a competitive market. The company's goal is to drive innovation and provide advanced solutions that meet the needs of a diverse clientele, including gamers, researchers, and enterprises.

Santa Clara, CaliforniaHeadquarters

1993Year Founded

$19.5MTotal Funding

IPOCompany Stage

Automotive & Transportation, Enterprise Software, AI & Machine Learning, GamingIndustries

10,001+Employees

Benefits

Company Equity

401(k) Company Match

Risks

Increased competition from AI startups like xAI could challenge NVIDIA's market position.

Serve Robotics' expansion may divert resources from NVIDIA's core GPU and AI businesses.

Integration of VinBrain may pose challenges and distract from NVIDIA's primary operations.

Differentiation

NVIDIA leads in AI and HPC solutions with cutting-edge GPU technology.

The company excels in diverse markets, including gaming, data centers, and autonomous vehicles.

NVIDIA's cloud services, like CloudXR, offer scalable solutions for AI and machine learning.

Upsides

Acquisition of VinBrain enhances NVIDIA's AI capabilities in the healthcare sector.

Investment in Nebius Group boosts NVIDIA's AI infrastructure and cloud platform offerings.

Serve Robotics' expansion, backed by NVIDIA, highlights growth in autonomous delivery services.

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