Meta Data Engineer Interview (2026)
Meta processes exabytes of data daily across Facebook, Instagram, WhatsApp, and their ads platform. Their DE interviews reflect this scale: heavy SQL with window functions, data modeling for consumer products, and system design that handles billions of events. Here is what each round tests and how to prepare.
Meta DE Interview Process
Six stages from first contact to offer. Each round tests a different skill set.
- 01
Recruiter Screen
Non-technical call covering your background, motivation for joining Meta, and role fit. The recruiter checks whether your experience aligns with the team and level. They will ask about scale: how much data you have worked with, what tools you used, and why Meta specifically.
- ▸Quantify data scale: row counts, daily volumes, GB/TB processed
- ▸Know Meta built Presto (now Trino), uses Spark heavily, processes exabytes daily
- ▸Ask which team the role is for; Meta DE roles vary across Ads, Integrity, Instagram, and Reality Labs
- 02
Technical Phone Screen
Live SQL coding, usually 1 to 2 problems. Meta phone screens lean on aggregation, window functions, and multi-step queries set in Meta-like contexts: user engagement, ad impressions, content moderation. The interviewer watches your problem-solving process as much as your final answer.
- ▸Think out loud. Meta grades your approach, not just the result
- ▸Expect window functions (ROW_NUMBER, LAG) combined with CTEs
- ▸Ask clarifying questions: NULL handling, duplicates, timestamp granularity
- 03
Onsite: SQL Deep Dive
Harder than the phone screen. Two to three SQL problems with increasing complexity. The first is a warm-up (basic aggregation). The second involves window functions or multi-step logic. The third may involve optimization: your query works, now discuss how to make it efficient at scale.
- ▸Practice writing SQL without autocomplete; Meta uses a shared document
- ▸If you finish early, the interviewer adds constraints (this is a good sign)
- ▸The optimization discussion tests awareness: indexing, partition pruning, avoiding unnecessary sorts
- 04
Onsite: Data Modeling
Design a data model for a Meta product: Facebook Events, Instagram Stories, Marketplace, or Messenger. Define fact and dimension tables, grain, slowly changing dimensions, and how the model supports specific analytical queries. This round tests whether you think about data as a system.
- ▸Start with the business question the model answers, then work backward to the schema
- ▸Define the grain explicitly: one row per user per day, one row per event, one row per impression
- ▸Discuss SCD Type 2 for dimensions that change over time
- 05
Onsite: System Design
Design a data pipeline at Meta scale. Examples: real-time ad metrics, content moderation event processing, cross-platform activity aggregation. The interviewer cares about reasoning at scale (billions of events per day), fault tolerance, data quality, and batch vs streaming tradeoffs.
- ▸Start with requirements: latency SLA, data volume, consumers
- ▸Mention partitioning, horizontal scaling, backpressure handling
- ▸Draw the architecture, even in a shared doc. Visual communication matters.
- 06
Onsite: Behavioral
Meta calls this the 'values' round. Questions focus on collaboration, conflict resolution, and impact. They want specific STAR format examples from your past work. Meta values 'Move Fast' and 'Build Social Value,' so frame examples around speed of delivery and user impact.
- ▸Prepare 4 to 5 stories that each demonstrate multiple values
- ▸Avoid generic answers; 'I communicated with the team' is not specific enough
- ▸Quantify impact: runtime reduction, cost savings, stakeholder satisfaction
10 Example Questions with Guidance
Real question types from each round. The guidance shows what the interviewer looks for.
Find users who logged in on 3 or more consecutive days.
Use LAG or the date-minus-ROW_NUMBER trick to create groups of consecutive days, then filter groups with COUNT >= 3. Tests window functions, date arithmetic, and grouping.
Calculate the rolling 7-day average of daily active users.
Aggregate to daily unique counts, then AVG with ROWS BETWEEN 6 PRECEDING AND CURRENT ROW. Mention you need a date spine to fill days with zero sessions.
Top 3 ads by click-through rate per campaign, excluding ads with fewer than 1000 impressions.
Calculate CTR, filter to impressions >= 1000, use ROW_NUMBER() OVER (PARTITION BY campaign ORDER BY ctr DESC), filter rn <= 3. Discuss filtering before vs after ranking.
Find the median number of reactions per post for each user.
Join posts to reactions, count per post, then PERCENTILE_CONT(0.5). If engine lacks median, use NTILE(2) or the ROW_NUMBER approach. Tests adaptability to engine constraints.
Design the data model for Facebook Events (create, invite, RSVP, attend).
Fact: rsvp_events (user_id, event_id, rsvp_status, timestamp). Dimension: events. Discuss RSVP status changes (SCD vs event sourcing), defining 'attendance', and aggregate tables for recommendations.
Model Instagram Stories data for analytics. Stories expire after 24 hours.
Fact: story_views. Dimension: stories (with expired_at). Discuss the 24-hour window, pre-aggregating view counts before expiration, and whether to keep raw events or only aggregates.
Design a pipeline for real-time ad click-through rates across all Meta properties.
Kafka for ingestion, Flink for stream processing, pre-aggregate by ad_id in sliding windows, serve from low-latency store. Discuss backfill strategy for stream outages.
Design a data quality monitoring system for Meta's data warehouse.
Schema validation, volume monitoring, distribution checks, freshness alerts. Discuss thresholds, handling expected anomalies (holidays, launches), and the feedback loop from consumers to producers.
Tell me about balancing speed of delivery against data quality.
Show a deliberate tradeoff: shipped V1 with known limitations, documented gaps, set up monitoring, iterated. Quantify: 'Launched 2 weeks earlier, caught 3 quality issues in week one via monitors.'
Describe improving the performance of an existing pipeline.
Specific before/after: runtime from 4 hours to 45 minutes, cost dropped 60%. Explain root cause diagnosis, changes made, and how you validated the output did not change.
Meta-Specific Preparation Tips
What makes Meta different from other companies.
Meta cares about scale
Every answer should acknowledge Meta's massive scale. When designing a pipeline, mention billions of events. When writing SQL, discuss performance on tables with hundreds of billions of rows. Scale awareness is the single biggest differentiator.
Know Meta's tech stack
Meta built Presto (now Trino) for interactive SQL. They use Spark for batch, Scuba for real-time analytics, and custom orchestration. Referencing these shows homework without requiring deep internal knowledge.
SQL uses Meta-like schemas
Expect tables named user_sessions, ad_impressions, content_interactions, friend_requests. Think about what data each Meta feature generates: every like, comment, share, impression, and scroll event is tracked.
Think metrics and experimentation
Meta is metrics-driven. Data engineers support A/B testing, metric computation, and experiment analysis. Mention how your pipeline supports experimentation: control vs treatment, metric slicing by variant.
Behavioral round has real weight
Some candidates over-prepare for technical rounds and under-prepare for behavioral. At Meta, the behavioral round can be a tiebreaker. Prepare specific stories demonstrating cross-team collaboration and shipping under deadlines.
Problems sourced from real Meta interview reports. Run your code in the browser.
Meta DE Interview FAQ
How many rounds are in a Meta DE interview?+
What SQL topics does Meta test most?+
Does Meta use LeetCode-style questions for DEs?+
What level are most Meta DE roles?+
How should I prepare for Meta's data modeling round?+
Prepare at Meta Interview Difficulty
Meta SQL questions start at intermediate and go to advanced. Practice with problems calibrated to that difficulty.