Data Engineering at Intel
Roles, Comp & Culture
An L5 senior data engineer at Intel sits around $259K total comp from 202 verified salary datapoints. The primary Data Engineering tech consists of Power BI, Azure and Fabric, according to current job listings. Intel pays data engineers slightly above other Technology companies. Reviews put them at 3.9 on Glassdoor, a little above the middle of the pack. Intel employees are stressed and employee happiness is trending down over the past year. Layoff risk scores moderate for the next 30 days. 4 data engineering roles are open right now.
Intel data engineer compensation
Each level's figure is the median of individual Intel 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.
Intel 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.
Intel's employer bargain right now is complicated by the company's broader strategic turbulence. Pay at L5 lands at $259K, which is slightly above other Technology companies, so the comp is competitive even as the company contracts. What you give up is stability of direction: mixed Blind sentiment and a stressed happiness tier trending down reflect an organization still working through a significant identity reset after years of market share erosion and leadership changes. 4 executive departures in the past 12 months adds to that uncertainty. Engineers who need clear organizational momentum will find the environment grating; those who can stay focused on technical problems through institutional noise may find the comp-to-competition ratio worthwhile.
Recent Intel events
Layoffs, leadership changes, and other major moves at the company, with dates.
The trajectory here is cautious. Intel is in a cost-reduction and refocus cycle, which shows up in hiring: 4 open data engineering roles across 4 cities, concentrated in Phoenix, a manufacturing and operations hub rather than a product center. That geographic footprint suggests the roles being filled are production-support and manufacturing analytics, not greenfield platform work. no tracked layoffs in the past 12 months, which is a real data point, but the broader company has been under sustained pressure with restructuring announcements through 2024 and 2025. Someone joining now is entering during a stabilization attempt, not an expansion. The next 12 months will depend heavily on whether the foundry strategy gains traction; the data engineering headcount will likely follow that signal closely.
- Exec departureApr 2026Leadership change
- Exec departureApr 2026Leadership change
- Exec departureNov 2025Leadership change
- Exec departureSep 2025Leadership change
- LayoffJul 2025~174 roles cut
- Exec departureApr 2025Leadership change
- Exec departureMar 2025Leadership change
- Exec departureFeb 2025Leadership change
- Exec departureDec 2024Leadership change
Notable company events we track, with dates.
Intel data engineering tech stack
The languages, storage, and processing tools Intel data engineers actually work with, grouped by what they do. Tailor your system-design answers to this stack.
Intel's data problem is manufacturing scale applied to silicon. Fabs generate telemetry at a volume most data engineers never encounter: yield data, equipment sensor streams, defect imaging, and supply chain signals across a global production network. A data engineer here is likely building pipelines that feed yield analysis and manufacturing intelligence, not marketing attribution or recommendation systems. The visible stack, Power BI, Azure and Fabric alongside Python and SQL, points toward a Microsoft-aligned infrastructure, which means the day-to-day involves structured warehouse work and BI delivery more than streaming or lakehouse architecture. If you're drawn to the intersection of physical manufacturing and data infrastructure, the domain is genuinely distinctive; if you want consumer-scale event pipelines, the work here skews elsewhere.
SQL
FabricIntel data engineer job openings
A live read on what they are hiring: open roles, recent postings, where, and at what level.
Engineers who fit Intel's current mode are mid-level and patient. The salary reports cluster heavily at L4, with 196 reports at $184K, which tells you this is where the actual hiring volume sits. Senior candidates can find a path, but the 6 reports at 6 suggest it's not the dominant band being filled. You'll do well here if you're comfortable in a large enterprise environment, value manufacturing-domain depth over breadth of tooling, and can tolerate organizational ambiguity without needing frequent signal that things are going well. If you want a fast-moving data platform team with clear product ownership, Intel is probably the wrong call right now. If the profile fits, prep the loop with attention to pipeline architecture, since that's where the technical evaluation goes deep.
Preparing for the Intel loop
The round-by-round process, example questions, and prep plan are on the interview guide.
Compare Intel with other data engineering employers
How the role, pay, and loop stack up against peer companies.
Prepare at Intel 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