Company Interview Guide
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.
Six stages from first contact to offer. Each round tests a different skill set.
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.
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.
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.
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.
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.
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.
Real question types from each round. The guidance shows what the interviewer looks for.
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.
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.
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.
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.
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.
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.
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.
Schema validation, volume monitoring, distribution checks, freshness alerts. Discuss thresholds, handling expected anomalies (holidays, launches), and the feedback loop from consumers to producers.
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.'
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.
What makes Meta different from other companies.
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.
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.
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.
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.
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.
Meta SQL questions start at intermediate and go to advanced. Practice with problems calibrated to that difficulty.
Practice Meta-Level SQL