NVIDIA · nvidia.wd5.myworkdayjobs.com · checked today
Senior Full Stack Software Engineer, Data Platform
NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing.
Skills, with evidence
- Python
Bachelor’s or Master’s degree or equivalent experience in Computer Science, Information Systems, or a related field; strong programming foundation in JavaScript/TypeScript, Python, database architecture development, and web application development
must have - Data modelling
Strong experience owning and managing full stack applications that go from the front end to backend processing and manage complex datasets as part of a data tools solution
must have - AWS
Familiarity with AWS/cloud architecture, Linux, virtual machines, clusters, and application monitoring/troubleshooting
not practised here - Java
Experience working with technologies such as JavaScript/TypeScript, Svelte/SvelteKit, Vue, React, D3, PHP, Node.js, Python, Apache, Nginx, PostgreSQL, Athena, Redshift
not practised here - Cost & performance
Build, scale, and optimize full stack software tools
- Failure handling
Familiarity with AWS/cloud architecture, Linux, virtual machines, clusters, and application monitoring/troubleshooting
- SQL
Experience working with technologies such as JavaScript/TypeScript, Svelte/SvelteKit, Vue, React, D3, PHP, Node.js, Python, Apache, Nginx, PostgreSQL, Athena, Redshift
- Kubernetes
Background with Docker containers, microservice architecture, and AWS
not practised here
Your plan
- The SQL screen: correct, then fast≈ 2 h
SQL
- Median delivery time per cityIntermediate
- Bucket deliveries into quartilesIntermediate
- Median order value without a median functionIntermediate
- New and repeat orders by monthIntermediate
- Every order against its customer's averageIntermediate
- Python: the data-wrangling round≈ 3 h
Python
- Diff two snapshots of a tableIntermediate
- Explode an array column into rowsIntermediate
- Flatten nested event payloadsIntermediate
- Pivot a long metrics table to wideIntermediate
- Choose what an incremental run should readIntermediate
- Data modelling: the round most people fail≈ 3 h
Data modelling
- Addresses that stay true to the pastIntermediate
- Seat holds and the release-night raceIntermediate
- Subscription warehouse grainIntermediate
- Campaign efficiencyIntermediate
- Catalogue: products, variants and sellersIntermediate
- Pipeline design: safe to run twice≈ 4 h
Failure handling
- Is this change safe?Intermediate
- The source will not let youIntermediate
- Changing a pipeline that’s already runningIntermediate
- SLA-aware alerting flowIntermediate
- The table everyone depends onIntermediate
- Spark: read the plan Spark actually ran≈ 2 h
Cost & performance
- applyInPandas per country: what Spark ships to Python, and the native rewriteAdvanced
- Filters you wrote in the wrong place: where Catalyst moves themAdvanced
- snappy, gzip or zstd: measure the Parquet codec trade-off yourselfAdvanced
- Find the four suspects in a slow pipeline (one of them is innocent)Advanced
- A filter after a window function: pushed down or not?Advanced
- Say it out loud≈ 1 h
25 drills · Intermediate + Advanced≈ 14 hours
Not covered by the plan: AWS, Java, Kubernetes.
Readiness
Counted from drills you have completed anywhere on D8LooP.
leaves in 11 dremoved the moment NVIDIA closes it