We are looking for a Senior Data Engineer to join the manufacturing data Engineering team and help us craft data collection solutions, quality control methodologies and monitors, support data enlargement scale. As a Data Engineer, you will play a significant role in bridging the gap between machine learning and software engineering. Your primary responsibility will be to develop, deploy, and maintain the infrastructure, data pipelines and workflows required for the end-to-end solution collecting the data till loading to data warehouse. If you are genuinely passionate about data engineering platforms, methodologies and possesses excellent problem-solving, we'd love to hear from you!
What you’ll be doing:
Collaborate with multi-functional teams, including full stack engineers, data scientists, data engineers, and DevOps, to craft, implement robust reliable data pipelines, infrastructure and workflows.
develop and support data models, schemas, and database structures that provide blazing-fast analytics performance. Constantly optimize data workflows to ensure flawless real-time and batch data processing.
Implement data quality checks and monitoring mechanisms to ensure data integrity and accuracy at every stage. detect and resolve bottlenecks swiftly to maintain a highly available and reliable data ecosystem.
Control, monitor and optimize worldwide production servers.
Build and maintain scalable data pipelines, ensuring efficient data storage, retrieval, and transformation.
Stay ahead of the curve in data engineering technologies and trends. Introduce new tools, techniques, and standard methodologies to improve our data infrastructure, empowering data scientists and analysts alike.
Feed a culture of learning and knowledge-sharing within the team. Guide and mentor junior data engineers, applying your expertise to uplift the entire department.
What we need to see:
Bachelor’s or master’s degree in computer science, Engineering, or a related field.
5+ years of relevant experience.
Demonstrated track record as a hands-on Data Engineer, driving the successful delivery of sophisticated data projects. Your experience speaks volumes.
Experience with cloud-based data platforms like AWS, Azure, or GCP. You harness the power of the cloud to unlock data's full potential.
Strong programming skills in languages such as Python or Scala, with experience in building scalable and efficient systems.
Experience with containerization technologies like Docker and orchestration tools like Kubernetes.
Solid knowledge in Linux environment.
Ways to stand out from the crowd:
Attention to detail is unwavering. You take pride in delivering data of the highest quality, turning it into insights that power critical business decisions.
Experience working and data engineering frameworks such as Kafka, Cloudera, Spark, Airflow or Hadoop.
Growing in a multifaceted environment, able to balance multiple priorities admirably without compromising on quality or precision.
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. NVIDIA is committed to fostering a diverse 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.
Skills Required
- Bachelor's or Master's degree in Computer Science, Engineering, or related field.
- 5+ years of relevant data engineering experience.
- Proven hands-on experience delivering complex data engineering projects.
- Experience with cloud-based data platforms (AWS, Azure, or GCP).
- Strong programming skills in Python or Scala.
- Experience with containerization (Docker) and orchestration (Kubernetes).
- Solid knowledge of Linux environments.
- Experience with data modeling, schema design, and data warehouses.
- Experience with Kafka, Cloudera, Spark, Airflow, or Hadoop.
- Attention to detail and strong data quality focus.
- Experience mentoring or guiding junior data engineers.
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
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