Nvidia
Hardware and AI software leader powering the global generative AI revolution.
4 Rounds
~25 Days
Very Hard
The Interview Loop
Recruiter Screen (30 min)
Standard fit check, behavioral questions, and resume overview.
Technical Loop (3-4 Rounds)
Deep dive into domain knowledge, coding, and system design.
Interview Question Bank
Data Engineer
•
Behavioral
•
medium
Tell me about a time you had to push back on a data requirement from a Data Scientist or Machine Learning Engineer because it was not feasible or scalable.
#Communication
#Stakeholder Management
#Prioritization
Data Engineer
•
Behavioral
•
easy
Describe a time you identified a bottleneck in a slow-running data pipeline. How did you diagnose the issue, and what steps did you take to optimize it?
#Performance Tuning
#Problem Solving
#Impact
Data Engineer
•
Behavioral
•
medium
Tell me about a time you had to optimize a slow-running data pipeline. What steps did you take to identify the bottleneck, and what was the impact?
#Performance Optimization
#Problem Solving
Data Engineer
•
Behavioral
•
medium
Describe a situation where you strongly disagreed with a senior engineer or architect on a system design choice. How did you handle it?
#Communication
#Conflict Resolution
#Collaboration
Data Engineer
•
Behavioral
•
medium
Nvidia moves at a very fast pace. Tell me about a time you had to deliver a critical data project with highly ambiguous requirements and a tight deadline.
#Adaptability
#Time Management
#Agile
Data Engineer
•
Behavioral
•
medium
Tell me about a time a data pipeline you built failed in production. How did you troubleshoot it, and what did you do to prevent it from happening again?
#Incident Management
#Reliability
#Post-mortems
Data Engineer
•
Behavioral
•
easy
How do you stay updated with the rapidly evolving landscape of data engineering, AI, and cloud technologies?
#Continuous Learning
#Industry Trends
Data Engineer
•
Coding
•
hard
Write a SQL query to find the top 3 consecutive days where GPU utilization exceeded 90% across our data centers.
#Window Functions
#CTEs
#Gaps and Islands
Data Engineer
•
Coding
•
medium
Given an array of GPU job execution intervals where intervals[i] = [start_i, end_i], merge all overlapping intervals and return an array of the non-overlapping intervals that cover all the jobs.
#Arrays
#Sorting
#Python
Data Engineer
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Coding
•
medium
Design and implement a Least Recently Used (LRU) cache in Python. This is often used in our data access layers to cache frequently queried model metadata.
#Data Structures
#Hash Map
#Doubly Linked List
Data Engineer
•
Coding
•
medium
Write a SQL query to calculate the rolling 7-day average of daily active users (DAU) accessing our cloud gaming platform (GeForce NOW), optimized for a massive dataset.
#Window Functions
#Aggregations
#Performance
Data Engineer
•
Coding
•
medium
Given a massive log file of error codes generated by our DGX systems that cannot fit into memory, write a Python script to find the top K most frequent error codes.
#Python
#Heaps
#File I/O
#Memory Management
Data Engineer
•
Coding
•
medium
Given a list of intervals representing GPU job execution times (start_time, end_time), write a Python function to merge all overlapping intervals.
#Python
#Arrays
#Sorting
Data Engineer
•
Coding
•
medium
Write a SQL query to calculate the 7-day rolling average of GPU utilization percentage for each cluster in our data center.
#Window Functions
#Time Series
#Aggregations
Data Engineer
•
Coding
•
medium
Write a Python script to parse a complex, deeply nested JSON payload from a REST API and flatten it into a tabular format suitable for insertion into a relational database.
#Python
#JSON
#Recursion
#Pandas
Data Engineer
•
Coding
•
hard
Write a SQL query to identify 'sessions' of user activity on the Nvidia Developer portal. A new session starts if there is a gap of more than 30 minutes between actions.
#Gaps and Islands
#Window Functions
#Date/Time Functions
Data Engineer
•
Coding
•
medium
Given a massive log file containing billions of error codes, write a Python program to find the top K most frequent error codes. The file is too large to fit in memory.
#Python
#Heaps
#External Sorting
#Generators
Data Engineer
•
Coding
•
medium
Write a SQL query to find the top 3 longest-running AI training jobs in each department, including ties.
#Window Functions
#Ranking
Data Engineer
•
Coding
•
medium
Write a SQL query to calculate the cumulative sum of terabytes processed per day by a specific pipeline, but the sum must reset to zero at the beginning of each month.
#Window Functions
#Aggregations
Data Engineer
•
Coding
•
hard
Implement an LRU (Least Recently Used) Cache in Python. This is often used to cache database lookups in our ingestion layer.
#Python
#Data Structures
#Hash Maps
#Linked Lists
Data Engineer
•
Coding
•
medium
Write a SQL query to find all pipelines that have failed on three or more consecutive days.
#Window Functions
#Self Joins
#Advanced SQL
Data Engineer
•
Coding
•
medium
Write a Python function to implement a Rate Limiter using the Token Bucket algorithm. This is used to throttle API requests to our internal data services.
#Python
#System Design Concepts
#Concurrency
Data Engineer
•
Coding
•
hard
Write a SQL query to calculate the 30-day retention rate of developers using the Nvidia Developer portal. Retention is defined as a user logging in on day 0 and also logging in on day 30.
#Cohort Analysis
#Self Joins
#Date Math
Data Engineer
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Coding
•
hard
Given a list of task dependencies (e.g., Task A must finish before Task B), write a Python function to determine a valid execution order for the tasks. If there is a circular dependency, return an error.
#Graphs
#Topological Sort
#Python
Data Engineer
•
System Design
•
hard
Design a real-time data ingestion and processing pipeline to collect and analyze telemetry data (temperature, power draw, utilization) from 100,000 GPUs globally.
#Streaming
#Kafka
#Scalability
#Time-series Databases
Data Engineer
•
System Design
•
hard
Design an ETL pipeline to process petabytes of autonomous vehicle sensor data (images, LIDAR, radar) stored in S3 to prepare it for deep learning model training.
#Batch Processing
#Data Lakes
#Distributed Storage
#ETL
Data Engineer
•
System Design
•
medium
Design a dimensional data model (Star Schema) for tracking AI model training experiments, including hyperparameters, epoch metrics, and final accuracy scores.
#Star Schema
#Data Warehousing
#Fact/Dim Tables
Data Engineer
•
System Design
•
hard
Design a real-time data pipeline to ingest, process, and visualize hardware telemetry data (temperature, clock speed, memory usage) from millions of Nvidia GPUs globally.
#Streaming
#Kafka
#Apache Flink
#Time-Series Databases
Data Engineer
•
System Design
•
hard
Design a batch processing system to clean, tokenize, and prepare petabytes of raw text data for training a Large Language Model (LLM).
#Batch Processing
#MapReduce
#Spark
#Data Quality
#AI/ML Pipelines
Data Engineer
•
System Design
•
medium
Design a data warehouse architecture for Nvidia's GeForce NOW gaming service to track user latency, frame drops, and session quality across different global regions.
#Data Warehousing
#Star Schema
#OLAP
#Snowflake/Databricks
Data Engineer
•
System Design
•
hard
Design a system to deduplicate a continuous stream of billions of telemetry events per day in real-time.
#Streaming
#Deduplication
#Bloom Filters
#Redis
Data Engineer
•
System Design
•
hard
Design an anomaly detection pipeline for manufacturing defect logs coming from semiconductor fabrication plants. The system needs to alert engineers within seconds.
#Real-time Analytics
#Machine Learning
#Alerting
#Pub/Sub
Data Engineer
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System Design
•
medium
Design a scalable distributed web crawler to scrape hardware pricing and availability data from thousands of e-commerce sites daily.
#Distributed Systems
#Web Crawling
#Message Queues
#Databases
Data Engineer
•
System Design
•
medium
Design a leaderboard system for an internal Kaggle-like competition platform used by Nvidia AI researchers. It needs to handle frequent score updates and fast rank retrievals.
#Redis
#Caching
#Databases
Data Engineer
•
Technical
•
hard
In Apache Spark, you are joining a massive telemetry fact table with a smaller dimension table, but the job keeps failing with an OutOfMemory (OOM) error due to data skew. How do you troubleshoot and resolve this?
#Apache Spark
#Performance Tuning
#Data Skew
Data Engineer
•
Technical
•
medium
Explain the architectural differences between Apache Spark and Nvidia RAPIDS (cuDF). When would you choose to accelerate an ETL pipeline using GPUs over a traditional CPU-based Spark cluster?
#GPU Acceleration
#RAPIDS
#Spark
#ETL
Data Engineer
•
Technical
•
medium
We use Apache Airflow for orchestration. How would you design a DAG to handle a scenario where an upstream API fails intermittently, and how do you manage backfilling data for the missed days once the API is restored?
#Apache Airflow
#Idempotency
#Error Handling
Data Engineer
•
Technical
•
hard
Explain how you would achieve exactly-once processing semantics in a Kafka to Spark Streaming pipeline. What are the trade-offs?
#Apache Kafka
#Spark Streaming
#Distributed Systems
Data Engineer
•
Technical
•
medium
What is the difference between narrow and wide transformations in Spark? Provide examples of each and explain how they impact the Catalyst Optimizer's physical execution plan.
#Apache Spark
#Distributed Computing
#Catalyst Optimizer
Data Engineer
•
Technical
•
hard
Explain how Apache Spark handles data skewness. How would you resolve a severely skewed join in a pipeline processing terabytes of autonomous driving sensor data?
#Apache Spark
#Performance Tuning
#Distributed Computing
Data Engineer
•
Technical
•
easy
What is the difference between `repartition()` and `coalesce()` in Apache Spark? When would you use one over the other?
#Apache Spark
#Data Shuffling
#Resource Management
Data Engineer
•
Technical
•
hard
How do you guarantee exactly-once processing semantics in an Apache Kafka to Apache Flink (or Spark Structured Streaming) pipeline?
#Apache Kafka
#Streaming
#Distributed Systems
Data Engineer
•
Technical
•
medium
Explain the Global Interpreter Lock (GIL) in Python. How does it impact the performance of multithreaded data processing scripts, and how can you bypass it?
#Python
#Concurrency
#Multithreading
#Multiprocessing
Data Engineer
•
Technical
•
medium
How do you design an Apache Airflow DAG to handle backfilling for a year's worth of data without overwhelming the source production database?
#Apache Airflow
#Data Pipelines
#Database Load Management
Data Engineer
•
Technical
•
hard
Have you used GPU-accelerated data processing frameworks like RAPIDS (cuDF)? How does memory management differ between CPU-based Pandas/Spark and GPU-based dataframes?
#RAPIDS
#GPUs
#Memory Management
#CUDA
Data Engineer
•
Technical
•
easy
Compare Parquet and ORC file formats. Why is Parquet generally preferred for analytical workloads and machine learning pipelines?
#File Formats
#Big Data
#Columnar Storage
Data Engineer
•
Technical
•
medium
How do you handle schema evolution in a streaming data pipeline where upstream microservices frequently add, remove, or change fields?
#Schema Evolution
#Avro
#Protobuf
#Data Contracts
Data Engineer
•
Technical
•
medium
Explain the mechanics of Spark's Catalyst Optimizer. How does it transform a logical plan into a physical plan?
#Apache Spark
#Query Optimization
Data Engineer
•
Technical
•
medium
How do you ensure data quality and implement data contracts in a microservices-driven architecture where data engineers don't control the source applications?
#Data Contracts
#Data Governance
#Microservices
Data Engineer
•
Technical
•
medium
Explain the difference between Kimball and Inmon data warehousing methodologies. Which approach would you choose for a centralized telemetry data warehouse at Nvidia, and why?
#Data Warehousing
#Kimball
#Inmon
#Architecture
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