About this Manager, Data and AI Engineering role at NVIDIA
We are looking for a Manager of Data & AI Engineering who combines deep technical expertise with strong delivery leadership and people management. This role will drive the build-out of our next-generation autonomous data intelligence platform for Supply Chain Operations — from identifying high-impact opportunities to architecting, building, and productionize solutions that deliver measurable business value. The ideal candidate brings hands-on experience in architecture and engineering while demonstrating the ability to manage a high-performing team. This person will partner with business collaborators, translate operational challenges into data and AI solutions, and deliver at pace.
What you'll be doing:
Design and build scalable data and AI platforms using Databricks, AWS, and modern cloud-native engineering patterns.
Deliver robust 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, and production-ready solutions.
Oversee resource planning, prioritization, project execution, and delivery across multiple concurrent initiatives, and 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, and handle the full lifecycle of data engineering projects — from discovery and planning through execution and production rollout.
What we need to see:
Master's or Bachelor's degree in Computer Science or Information Systems, or equivalent experience
10+ overall years in Data Engineering, Software Engineering, or web application development, with at least 3+ years specifically in a leadership or engineering management role.
Willingness to Code: You are still a builder at heart. You are excited to spend your time writing code, prototyping, and building production systems alongside your team.
AWS Proficiency: Intimate knowledge of the AWS ecosystem, including Amazon S3, EC2, IAM, Lambda, and API Gateway.
Agentic AI & LLM Mastery: Proven experience operationalizing Large Language Models (LLMs) into autonomous agents that can plan, use tools, and implement multi-step workflows.
Databricks Mastery: Proven deep expertise in Apache Spark, PySpark, Delta Lake, and Databricks Workflows. Hands-on experience scaling Unity Catalog is highly preferred.
Ways to stand out from the crowd:
Active Databricks Certifications (e.g., Data Engineer Professional, Generative AI Engineer Associate).
Active AWS Certifications (e.g., Certified Data Engineer – Associate or Solutions Architect – Professional).
Background in 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 & Services
You will also be eligible for equity and benefits.
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.