Software Engineer, Technical Lead – Performance Data Platform Staff

About this role

NVIDIA · nvidia.wd5.myworkdayjobs.com · checked today

Software Engineer, Technical Lead – Performance Data Platform

US, CA, Santa ClaraStaff*15+ yrs*Data Platformposted 1 d ago

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

Proficiency with C++, Go, Python.must have

• Machine learning

Practical knowledge of GPU and machine learning workload performance: you can review a performance diagnosis, question its evidence, and decide which signals matter most.must have · not practised here

• Data modelling

A track record of crafting APIs and data models that evolved without breaking existing users or deployments.must have

• SQL

Working knowledge of SQL and TypeScript.

• Cost & performance

Describe what queries they may perform, how their results relate to evidence, and the constraints on their cost and response time.

• Governance & security

Identity, access control and multi-tenant build for products that hold customers’ data.not practised here

Your plan

1. The SQL screen: correct, then fast

SQL

• Amazon: net revenue across two fan-outsAdvanced
• Build a percentile tableAdvanced
• Interview top buyers with tiesAdvanced
• Netflix: a 7-day unique count you cannot add upAdvanced
• Swiggy: the p90 the average is hidingAdvanced

≈ 2 h

1. Python: the data-wrangling round

Python

• Apply a change-data-capture batchAdvanced
• Deduplicate a stream in bounded memoryAdvanced
• Compact segments without resurrecting deletesAdvanced
• Detect gaps in a partitioned offset streamAdvanced
• Commit batches to a sink exactly onceAdvanced

≈ 4 h

1. Data modelling: the round most people fail

Data modelling

• Claims, reserves, and payments that reverseAdvanced
• Customer 360 tradeoffsAdvanced
• Datasets, owners, and access grantsAdvanced
• Care episodes, providers, and overlapping responsibilitiesAdvanced
• Loads, drivers, and backhauls that vanishAdvanced

≈ 4 h

1. Spark: read the plan Spark actually ran

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

≈ 2 h

1. Say it out loud

Rehearse: SQL · Query formulation

≈ 1 h

20 drills · Advanced + Intermediate≈ 13 hours

Not covered by the plan: Machine learning, Governance & security.

Readiness

0 of 20 drills already done elsewhere on D8LooP.

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