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NVIDIA

Designs the chips and software that power AI

Manager – Data and AI Engineering

Engineering LeadershipConfirmed live in the last 24 hours
Full-timeIn-person

About the Job

Requirements

  • A master's or bachelor's degree in Computer Science or Information Systems, or equivalent experience.
  • At least 10 years of experience in Data Engineering, Software Engineering, or web application development, including at least 4 years in a leadership or engineering management role.
  • Willingness to write code, prototype, and build production systems alongside the team.
  • Proficiency with the AWS ecosystem, including Amazon S3, EC2, IAM, Lambda, and API Gateway.
  • Proven experience operationalizing Large Language Models into autonomous agents that plan, use tools, and implement multi-step workflows.
  • Deep expertise in Apache Spark, PySpark, Delta Lake, and Databricks Workflows.

Responsibilities

  • Design and build scalable data and AI platforms using Databricks, AWS, and modern cloud-native engineering patterns.
  • Deliver ETL/ELT, streaming, and CDC pipelines using technologies such as Spark, Kafka, Delta Lake, and AWS-native services.
  • Enable delivery of AI-powered use cases, including RAG applications, AI agents, tool-calling workflows, and data-driven web apps.
  • Design data models using Star Schema, Snowflake Schema, and Data Vault patterns appropriate to the use case, optimizing for analytical query performance, data governance, and extensibility.
  • Implement data quality frameworks, observability, alerting, and monitoring to ensure pipeline integrity and production reliability.
  • Build the data foundation for GenAI, agentic AI, and advanced analytics initiatives, including RAG pipelines, vector search, knowledge graphs, and multi-agent orchestration patterns.
  • Partner with product, business, analytics, and AI collaborators to translate requirements into secure, scalable, production-ready solutions.
  • Oversee resource planning, prioritization, project execution, and delivery across multiple concurrent initiatives.
  • Mentor engineers, grow technical capability across the team, and develop a culture of accountability, innovation, and continuous improvement.
  • Provide hands-on technical leadership across architecture, design reviews, implementation guidance, and production readiness.
  • Handle the full lifecycle of data engineering projects, from discovery and planning through execution and production rollout.

Desired Qualifications

  • Hands-on experience scaling Unity Catalog.
  • Active Databricks certifications, such as Data Engineer Professional or Generative AI Engineer Associate.
  • Active AWS certifications, such as Certified Data Engineer – Associate or Solutions Architect – Professional.
  • Background managing multi-functional teams that blend data engineers with front-end and back-end software developers.
  • Knowledge of supply chain business processes for Plan, Make, Deliver, and Services.

About the Company

NVIDIA

Designs the chips and software that power AI

NVIDIA designs the chips and software that power artificial intelligence and sells complete data center systems to cloud providers, AI labs, and other businesses, along with GeForce graphics cards for gamers. Its GPUs, chips that perform thousands of calculations at once, became the main hardware for AI after researchers used them to train the AlexNet image-recognition model in 2012. NVIDIA connects many GPUs with its own processors and networking gear into servers and rack-sized computers, and developers program them through its CUDA software. These systems bring in most of its sales, and it also makes chips for cars and robots. Jensen Huang co-founded NVIDIA in 1993, and it is based in Santa Clara, California.

Company size
10,001+
Company stage
IPO
Headquarters
Santa Clara, CA
Founded
1993

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