Data Engineering at Meta
Roles, Comp & Culture
An L7 principal data engineer at Meta sits around $660K total comp from 774 verified salary datapoints. The ladder runs from about $168K at entry up to $660K. Meta pays data engineers above other Technology companies. Reviews put them at 3.7 on Glassdoor (a little below the middle of the pack) and negative sentiment on Blind. Meta employees are stressed and employee happiness is trending down over the past year. Layoff risk scores moderate for the next 30 days. 19 data engineering roles are open right now.
Meta
Technology · Menlo Park, US · META
live data · August 2, 2026
DE total comp
$342K median
L5 · senior level · $286K–$420K · 322 verified datapoints
Hiring now
19 open DE roles
live from career pages
Team happiness
Stressed
employee happiness
Layoff risk (30d)
Moderate
Employee sentiment
3.7 / 5
NegativeEmployees
501–1,000
Meta data engineer compensation
Each level's figure is the median of individual Meta 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.
What the Meta signals mean
Sentiment reflects a company that pays at the top of the market but has run repeated performance-based cuts and a tighter rating culture, moving to a 4-tier Checkpoint scale in 2026. The trade-off candidates weigh is compensation and scope against intensity and reorg risk.
Meta 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.
Recent Meta events
Layoffs, leadership changes, and other major moves at the company, with dates.
- LayoffJul 2026~252 roles cut
- LayoffMay 2026~74 roles cut
- LayoffMay 2026Layoff
- LayoffMay 2026~168 roles cut
- Exec departureApr 2026Leadership change
- LayoffMar 2026~53 roles cut
- Exec departureJan 2026Leadership change
- LayoffDec 2025~1,500 roles cut
- Exec departureDec 2025Leadership change
- LayoffOct 2025~600 roles cut
- Exec departureApr 2025Leadership change
- Exec departureFeb 2025Leadership change
- Exec departureJan 2025Leadership change
Notable company events we track, with dates.
Meta data engineering tech stack
The languages, storage, and processing tools Meta data engineers actually work with, grouped by what they do. Tailor your system-design answers to this stack.
SQLMeta data engineer job openings
A live read on what they are hiring: open roles, recent postings, where, and at what level.
Design, build, and launch collections of sophisticated data models and visualizations that support use cases across different products or domains
Design, build, and launch collections of sophisticated data models and visualizations that support multiple use cases across different products or domains
Design and scale the data pipelines and instrumentation that capture agent telemetry, usage signals, and outcome metrics across a fragmented and fast-moving tool landscape
Candidates should also have a proven track record of leading and scaling efforts related to end-to-end analytics systems, operational skills to drive efficiency and speed, project management leadership, and a vision for how data can proactively improve companies.
In this role, you will collaborate with software engineering, data science, and product management teams to design/build scalable data solutions across Meta to optimize growth, strategy, and user experience for our 3 billion plus users, as well as our internal employee community.
Practice for the Meta loop
Round by round, the problems our model predicts for this company's interview. Rehearse the shapes their panels keep returning to.
What makes Meta different
The things about this company that should shape every answer you give.
A dedicated data modeling round
Most FAANG peers embed data modeling inside a system design or SQL round. Meta runs it as its own 45-minute round: design fact and dimension tables for a real product, define the grain explicitly, and reason about slowly changing dimensions. It is the single biggest structural differentiator of the loop.
Scale is the through-line of every answer
Meta operates at exabyte scale. When designing a pipeline, the expectation is billions of events per day; when writing SQL, performance on tables with hundreds of billions of rows; when optimizing, a named partition strategy. Scale awareness is what separates strong candidates from passing ones.
Metrics and experimentation are the job
Meta is metrics-driven, and DEs support A/B testing, metric computation, and experiment analysis. The strongest answers connect a pipeline back to experimentation: control vs treatment, metric slicing by variant, and statistical power for small-effect detection.
The values round can be the tiebreaker
Meta's behavioral round is framed around 'Move Fast' and 'Build Social Value,' and it carries real weight; it can decide a close loop. Candidates who over-index on technical prep and under-prepare specific, quantified collaboration stories lose offers here.
Preparing for the Meta loop
The round-by-round process, example questions, and prep plan are on the interview guide.
Meta 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 Meta with other data engineering employers
How the role, pay, and loop stack up against peer companies.
Prepare at Meta 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