Data Engineering at Nvidia
Roles, Comp & Culture
An L6 staff data engineer at Nvidia sits around $590K total comp from 107 verified salary datapoints. The primary Data Engineering tech consists of Databricks, AWS and GCP, according to current job listings. Nvidia pays data engineers above other Technology companies. Reviews put them at 4.6 on Glassdoor, among the highest of any company here. Nvidia employees are stressed and employee happiness is trending down over the past year. Layoff risk scores low for the next 30 days. 5 data engineering roles are open right now.
Nvidia
Technology · Santa Clara, US · NVDA
live data · July 31, 2026
DE total comp
$391K median
L5 · senior level · $375K–$407K · 22 verified datapoints
Hiring now
5 open DE roles
live from career pages
Team happiness
Stressed
employee happiness
Layoff risk (30d)
Low
Employee sentiment
4.6 / 5
MixedEmployees
5,001–50,000
Nvidia data engineer compensation
Each level's figure is the median of individual Nvidia offers at that level, so it reflects a typical outcome rather than an average pulled up by a few large packages. Total comp counts base salary plus equity and bonus annualized over the vest, and the range shown is the middle half of offers, with the top and bottom quarters trimmed off.
Nvidia employee sentiment, tracked weekly
Employee happiness for data engineers over the past year, so you can see which direction it is moving, not just where it sits today.
The employer bargain at Nvidia right now is genuinely complicated. Pay is above other Technology companies, and at $237K for entry-level the floor is high by any reasonable comparison. What you give up is harder to quantify: a 4.6 Glassdoor rating among the highest of the companies we track signals something structurally unusual, because the mixed Blind sentiment and a stressed happiness tier trending down suggest the day-to-day experience does not match the reputation. The most likely tension is pace: Nvidia's GPU dominance in AI has created enormous internal demand for data infrastructure, and teams are absorbing that growth faster than headcount has scaled. Engineers who join for the resume line and the comp should factor in that the workload is load-bearing.
Recent Nvidia events
Layoffs, leadership changes, and other major moves at the company, with dates.
1 tracked layoff in the past 12 months, the most recent in May 2026, and 4 executive departures over the same period. Those executive exits are worth watching: leadership churn at that rate during a high-growth period can mean reorganization pressure is trickling down to the data org, not just the C-suite. Hiring volume for data engineering is modest at 5 open data engineering roles across 2 cities, which suggests the team is not in aggressive expansion mode despite the company's headline growth. Nvidia's AI tailwinds are real, but the internal data org appears to be digesting existing scale rather than adding headcount to match it. Someone joining now is more likely to be filling a critical gap than entering a team that is building out new functions.
- LayoffMay 2026Layoff
- Exec departureApr 2026Leadership change
- Exec departureMar 2026Leadership change
- Exec departureJan 2026Leadership change
- Exec departureAug 2025Leadership change
- Exec departureMar 2025Leadership change
Notable company events we track, with dates.
Nvidia data engineering tech stack
The languages, storage, and processing tools Nvidia data engineers actually work with, grouped by what they do. Tailor your system-design answers to this stack.
Nvidia's data engineering problems start with the GPU itself. The company runs one of the most data-dense supply chains in semiconductors, manages telemetry from millions of deployed chips across cloud and enterprise customers, and operates a software platform in CUDA that generates usage signals at a scale most companies never see. A data engineer here is likely working across product analytics for the developer platform, training infrastructure observability, or internal supply chain forecasting, all of which demand serious pipeline reliability and low-latency data delivery. The visible stack of Databricks, AWS and GCP with Python, SQL and Scala points toward a team that has invested in cloud-native batch processing and is starting to reach for streaming as telemetry volumes grow. With 5 open data engineering roles across 2 cities, hiring is concentrated; the work is specialized, not broad.
SQL
IcebergNvidia data engineer job openings
A live read on what they are hiring: open roles, recent postings, where, and at what level.
Optimize data pipelines and analytics datasets for correctness, performance, scalability, reliability, and cost.
Build and optimize pipelines that extract insights from complex financial documents like SEC filings, contracts, and tax reports.
Security Data Pipelines: Design, build, and operate the ingestion and transformation pipelines that collect security telemetry and asset inventory from dozens of heterogeneous sources, and normalize them into one canonical model.
Pipeline Architecture & Integrity: Architect event-driven pipelines (Kafka) and develop new data models that ensure transactional integrity (ACID) for commercial events like invoices, payments, and adjustments.
Practice for the Nvidia loop
Round by round, the problems our model predicts for this company's interview. Rehearse the shapes their panels keep returning to.
The salary ladder here has 4 levels with 107 verified reports, and the distribution tells you where Nvidia actually hires: 37 mid-level reports and 44 junior reports make up the bulk, which means this is not a place chasing senior-heavy teams. Engineers who thrive here tend to have a strong Python and SQL foundation and a willingness to own pipeline architecture end to end under real pressure, given that the loop focuses on pipeline architecture. If you're an early or mid-career data engineer who can operate without much process scaffolding and values comp over culture stability, this is worth pursuing. If you need clear growth structure or a collaborative low-stress environment, the current happiness signals argue against it. Check the ladder, then prep the architecture rounds before you apply.
Preparing for the Nvidia loop
The round-by-round process, example questions, and prep plan are on the interview guide.
Nvidia data engineer roles by level
Level-specific pages: the comp, the bar, and what the loop tests at each seniority.
Comp, level expectations, and role-specific prep.
Comp, level expectations, and role-specific prep.
Comp, level expectations, and role-specific prep.
Comp, level expectations, and role-specific prep.
Comp, level expectations, and role-specific prep.
Compare Nvidia with other data engineering employers
How the role, pay, and loop stack up against peer companies.
Prepare at Nvidia interview difficulty
- 01
Reading a solution is not the same as writing one
Every engineer who has frozen on a query they had read a dozen times knows the gap. The only preparation that closes it is producing the answer yourself, under time, before the interview does it for you
- 02
76% of hiring managers reject on the coding task, not the resume
From HackerRank's 2024 Developer Skills Report. Candidates who look strong on paper still fail the live screen if they haven't done timed, executable practice
- 03
5 problem shapes cover 80% of data engineer loops
Dedup, sessionization, top-N-per-group, slowly-changing dimensions, partition tricks. Writing the shapes by hand turns the unfamiliar into pattern recognition