Meta Data Engineer Interview Questions (2026)

1. 1Recruiter screen30 minEasy

   Process overview, level calibration, and prep guidance — Meta recruiters actively coach on round formats.

2. 2Technical screen45 minHard

   Two DSA problems in 45 minutes on a plain shared editor with no autocomplete or execution.

3. 3Coding round ('Ninja')45 minHard

   Two more problems at loop difficulty; clean near-compilable code and verbalized complexity analysis expected.

4. 4System design ('Pirate')45 minHard

   Design a Meta-scale product system (feed, Stories, chat) with emphasis on read-heavy fan-out, caching, and data modeling.

5. 5Behavioral ('Jedi')45 minMedium

   Deep past-experience discussion on conflict, growth, and impact aligned to Meta values; graded as a real signal round.

1. Q1

   For a Meta-like data project, tell me about a time you missed a deadline. What did you do?

   MediumRound 8: BehavioralAccountability

   How to answer: Communicate early, reset scope or timeline, explain root cause, protect critical users, and improve planning afterward.

2. Q2

   Give an example of ambiguous requirements for a data product similar to Meta's dashboards and downstream ML features. How did you clarify them?

   MediumRound 8: BehavioralAmbiguity

   How to answer: Identify users, decisions, metric definitions, freshness needs, edge cases, and acceptance criteria before building.

3. Q3

   At Meta, data decisions often involve trade-offs. Tell me about a conflict with another engineer over product analytics warehouse or streaming logs, Spark/Hive, Presto, and internal orchestration

   MediumRound 8: BehavioralConflict Resolution

   How to answer: State both positions fairly, explain evidence gathered, describe the decision process, and show the relationship stayed healthy.

4. Q4

   Design cost controls for Meta's product analytics warehouse where query and pipeline spend is growing faster than usage

   MediumRound 6: System DesignCost and Performance Design

   How to answer: Measure cost by owner and workload, optimize scans and files, right-size compute, cache or materialize common aggregates, and enforce budgets.

5. Q5

   Design an event contract and schema registry process for Meta's high-volume product event stream producers and data consumers

   MediumRound 6: System DesignData Contracts

   How to answer: Define versioned schemas, compatibility rules, ownership, validation at ingestion, documentation, and a migration process for breaking changes.

6. Q6

   Design observability for Meta's critical engagements pipelines across freshness, quality, volume, and cost

   MediumRound 6: System DesignData Observability

   How to answer: Collect SLIs for freshness, completeness, validity, failure rate, latency, and spend; alert on symptoms and attach run-level lineage.

7. Q7

   Define data quality checks for Meta's event_id, member_id, post_id, surface, event_type, event_time pipeline before publishing to analysts

   MediumRound 5: ETL DesignData Quality

   How to answer: Check schema, nullability, uniqueness, referential integrity, volume anomalies, value ranges, freshness, and reconciliation against source totals.

8. Q8

   Design a Meta data system for feed engagement and ads conversion analytics with end-to-end latency of under 5 minutes

   HardRound 6: System DesignData System Design

   How to answer: Use durable event ingestion, streaming processing, curated storage, low-latency serving, monitoring, and replayable raw logs.

9. Q9

   Design a star schema for Meta's social graph and ads platform that supports reporting on meaningful engagements, ad conversions, and impression -> click -> engagement -> conversion

   MediumRound 4: Data ModelingDimensional Modeling

   How to answer: Define a clear fact grain for engagements, add conformed dimensions such as post, member, surface, and advertiser dimensions, and store additive measures separately from derived metrics.

10. Q10

    Design a daily ETL pipeline for Meta that ingests high-volume product event stream into product analytics warehouse for meaningful engagements reporting

    MediumRound 5: ETL DesignETL Architecture

    How to answer: Land raw data, validate schema, transform to curated tables, run data quality checks, publish aggregates, and monitor freshness and failures.

11. Q11

    A Meta ETL job failed halfway after writing partial data. Walk through the recovery design

    HardRound 5: ETL DesignETL Failure Recovery

    How to answer: Detect partial output, roll back or overwrite the affected partition, rerun from checkpoint, validate row counts, and publish only after atomic completion.

12. Q12

    How would you make Meta's Airflow DAG for engagements processing idempotent and safe to backfill?

    HardRound 5: ETL DesignETL Orchestration

    How to answer: Use deterministic input ranges, write to temporary paths, validate outputs, atomic swap/merge, and parameterize DAG runs by logical date.

13. Q13

    Design reconciliation logic for Meta's ETL so ad conversions in product analytics warehouse matches the operational source

    MediumRound 5: ETL DesignETL Reconciliation

    How to answer: Compare control totals by date and surface, track accepted tolerances, investigate deltas, and block publishing on material mismatches.

14. Q14

    For Meta, decide between ETL and ELT for transforming feed, messaging, ad, and engagement events. What factors drive your choice?

    MediumRound 5: ETL DesignETL vs ELT

    How to answer: Choose ELT when the warehouse/lakehouse can scale transformations cheaply; choose ETL when privacy, bandwidth, or source constraints require pre-load shaping.

15. Q15

    For Meta's privacy-sensitive data such as social graph identifier, tell me about a time you raised an ethics, privacy, or governance concern

    HardRound 8: BehavioralEthics and Privacy

    How to answer: Describe the concern, policy or risk, who you involved, the decision, and how the safer approach still met business needs.

Practice these with instant AI feedback in a live mock interview → Start a Meta Data Engineer mock

Be fast and correct on coding; for design, drive the conversation; prepare impact-focused behavioral stories

Indicative Data Engineer pay in India: ~₹10–45 LPA (role-level range, not a Meta-specific figure).

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