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Meta Data Engineer Interview Guide 2026

Complete Meta Data Engineer interview guide. Learn about the interview process, question types, and preparation tips. Practice 47+ real interview questions.

Topics: Meta, Data Engineer, interview guide, interview preparation, Meta interview

Author: PracHub

Published: 3/17/2026

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Meta Data Engineer Interview Guide 2026

Complete Meta Data Engineer interview guide. Learn about the interview process, question types, and preparation tips. Practice 47+ real interview questions.

4 min readUpdated Jun 15, 202646+ practice questions
46+
Practice Questions
2
Rounds
5
Categories
4 min
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Contents
TL;DRSample QuestionsAbout the Interview ProcessWhat to expectInterview processRecruiter screenTechnical screenVirtual onsite (full loop)Hiring committee reviewTeam matchingOfferWhat they testSQL (the core)Python (secondary)Data modeling and pipeline designProduct and metrics judgmentBehavioralHow to prepareFAQ
Practice Questions
46+ Meta questions
Meta Data Engineer Interview Guide 2026

TL;DR

Meta's 2026 Data Engineer loop is more SQL-centric and product-aware than a typical data engineering interview. The emphasis is on practical analytics engineering: writing business-facing SQL under time pressure, designing reliable datasets and pipelines, and showing that you understand how data work shapes product decisions. Compared with a general software role, Meta weights advanced SQL, data modeling, metrics judgment, and ownership more heavily than algorithmic problem-solving. A typical path runs recruiter screen → technical screen → virtual onsite → hiring committee review → team matching → offer. The full process commonly takes 4 to 8 weeks, though it can run longer because committee review and team matching are not always fast.

Interview Rounds
OnsiteTechnical Screen
Key Topics
Coding & AlgorithmsData Manipulation (SQL/Python)System DesignAnalytics & ExperimentationBehavioral & Leadership
Practice Bank

46+ questions

Estimated Timeline

1–2 weeks

Browse all Meta questions

Sample Questions

46+ in practice bank
Data Manipulation (SQL/Python)
1.

Tackle Python tasks under time pressure

MediumData Manipulation (SQL/Python)

In a 15-minute coding round, implement a small Python function or class to solve a well-scoped problem within about 5 minutes of coding.

  1. State 1–2 clarifying questions you would ask before coding.
  2. Enumerate important edge cases and how your solution handles them.
  3. Provide clean, readable code and explain your formatting choices.
  4. Briefly describe time and space complexity and outline a minimal test plan.
Solution
2.

Optimize SQL to minimize scans

MediumData Manipulation (SQL/Python)Coding

Given a large analytics query, refactor it to minimize table scans.

  1. Replace unnecessary CTEs that cause multiple scans with inline aggregations or derived tables where appropriate.
  2. Prefer GROUP BY aggregations when applicable and justify the execution plan.
  3. Explain how you would verify the number of scans using EXPLAIN and iterate on the plan.
  4. Start with a quick draft solution, then refine it to reduce scans and improve readability.
Solution
System Design
3.

Design batch and streaming ETL architecture

HardSystem Design

System Design: End-to-End Data Platform for Product Analytics (Batch + Near-Real-Time)

Context

Design a scalable data platform for a large consumer product with web and mobile clients. The platform must power daily product analytics (e.g., DAU/MAU, retention, funnels, cohorts, experiments) and near-real-time dashboards (<5 minutes end-to-end) while supporting backfills and rigorous data quality.

Assume tens to hundreds of millions of daily events and multiple upstream systems (client telemetry, backend logs, relational OLTP for user/account, and third-party data). You may reference common technologies (e.g., Kafka, Flink/Spark, object store + lakehouse table format, a cloud data warehouse), but focus on design choices and trade-offs.

Requirements

  1. Ingestion sources and formats
  • Identify sources (client events, backend logs, CDC from OLTP, third-party feeds) and wire formats (JSON/Protobuf/Avro on the wire; Parquet/Delta/Hudi/Iceberg in storage).
  1. Storage and compute architecture
  • Describe the messaging/streaming layer, raw landing, staging, and modeled layers, and the batch/streaming compute engines.
  1. Schema design by layer
  • Define schemas for raw ("bronze"), deduped/cleaned ("silver"), and modeled analytics ("gold"). Include a canonical event envelope, dimensions (users/devices/products/experiments), and fact tables (events, sessions, conversions).
  1. Table partitioning and clustering
  • Propose partitioning and clustering/sorting for each major table to optimize scan cost and latency.
  1. Idempotency, deduplication, and late/out-of-order events
  • Specify unique keys, event-time vs ingestion-time, watermarking, allowed lateness, and how to reconcile late data into aggregates.
  1. Update patterns and history
  • State which layers are append-only vs upsert/merge. Explain SCD1 vs SCD2 for dimensions, identity resolution (anonymous → logged-in), and how you will run backfills safely.
  1. Orchestration, dependencies, and failure recovery
  • Describe scheduling, dependency management, retries, checkpointing, and exactly-once/at-least-once guarantees.
  1. Aggregations for daily/hourly/rolling metrics
  • Define how to compute daily/hourly windows and rolling windows (e.g., 7/28-day active, retention, funnel steps), both in streaming and batch.
  1. Data quality and SLAs
  • Outline schema enforcement, validation tests, anomaly detection, freshness/completeness SLAs, and alerting.
  1. Trade-offs
  • Discuss latency vs cost vs complexity; lambda vs kappa patterns; when to pre-aggregate vs compute on read; and real-time store choices.
Solution
4.

Model entities for feed content and shares

HardSystem Design

Scenario

You are designing the data model for a social app’s News Feed that shows multiple content types (text, image, short video). Users can interact with content (view, like, comment, share).

Prompt

  1. Propose a data model for the core feed domain focusing on:
    • Users
    • Content (supporting text/image/video)
    • Interactions between users and content
  2. Identify key entities, relationships, and primary/foreign keys.
  3. In an analytics warehouse context, specify which tables are dimensions vs facts and list a few core fields.
  4. Follow-up: extend the model to support sharing (a user shares a piece of content to another user, to a group, or to an external channel).
Solution
Coding & Algorithms
5.

Solve SQL and Python coding tasks

MediumCoding & Algorithms

You are given a small library system with the following relational schema and several Python data-processing tasks. Answer the SQL questions and implement the described Python functions.

Part 1: SQL Questions

Assume the following tables:

Table: Books

  • book_id INT, primary key
  • title VARCHAR
  • condition ENUM('good', 'damaged', 'lost') — current physical condition of the book copy
  • copies INT — number of physical copies the library owns for this title
  • lifetime_value DECIMAL — total revenue (e.g., rental fees) generated by this title over its lifetime

Table: Loans

  • loan_id INT, primary key
  • book_id INT, foreign key to Books(book_id)
  • member_id INT, foreign key to Members(member_id)
  • loaned_at DATETIME
  • returned_at DATETIME NULL — NULL means the book has not yet been returned

Table: Members

  • member_id INT, primary key
  • invited_by INT NULL, foreign key to Members(member_id) — the member who invited this member, if any
  • reserved_copies INT — number of book copies this member currently has reserved

Write SQL queries for the following:

  1. Count good, not-yet-returned books
    Return a single integer: the number of book loans where the book is in condition = 'good' and the book has not yet been returned (i.e., the corresponding Loans.returned_at is NULL).

  2. Top 3 high-value books with many copies
    Return the top 3 books (their book_id and title) that have:

    • more than 10 copies (copies > 10), and
    • the highest lifetime_value among such books.
      Order results by lifetime_value descending, and if needed, break ties by book_id ascending. Limit the result to 3 rows.

  3. Largest absolute difference in reserved copies between inviter and invitee
    For each member who was invited by another member (invited_by is not NULL), consider the pair:

    • the inviter: Members.member_id = invited_by
    • the invitee: Members.member_id (the one who was invited)

    Each member has a reserved_copies value. For every inviter–invitee pair, compute the absolute difference:

    [ |\text{inviter.reserved_copies} - \text{invitee.reserved_copies}| ]

    Write a query that returns a single row with a single column giving the maximum such absolute difference over all valid inviter–invitee pairs.

Part 2: Python Coding Questions

Implement the following functions in Python. Aim for efficient, readable solutions.

Python Q1: Maximum reading score without consecutive books

You are given a list of non-negative integers scores, where scores[i] is the number of points a student earns by reading the i-th book in a sequence. The student may choose to read any subset of books, but cannot read two adjacent books in the sequence (they need rest between adjacent books).

Write a function:

from typing import List

def max_reading_score(scores: List[int]) -> int:
    """Return the maximum total score achievable without reading adjacent books."""

Constraints:

  • 0 <= len(scores) <= 10**5
  • 0 <= scores[i] <= 10**4

The function should return the maximum possible total score.

Python Q2: Validate a sequence of open/close log entries

You are given a list of log entries representing operations on resources. Each entry is a tuple (action, resource_id) where:

  • action is a string: either 'OPEN' or 'CLOSE'
  • resource_id is a string identifying the resource

A log is considered valid if all of the following hold:

  • A resource cannot be closed before it has been opened.
  • A resource cannot be opened twice in a row without being closed in between.
  • At the end of the log, all opened resources must have been closed (no resource remains open).

Write a function:

from typing import List, Tuple

def is_log_valid(logs: List[Tuple[str, str]]) -> bool:
    """Return False if the log sequence is inconsistent; True otherwise."""

Return False if the

Solution
6.

Write queries for follows and bookings

MediumCoding & Algorithms

You are given tables/logs from a consumer app. Solve the following independent tasks.

Part A — Active following as of a date (SQL)

You have a follow event log:

  • follow_events(follower_id, followee_id, event_type, event_ts)
  • event_type ∈ {'follow','unfollow'}
  • A user can follow/unfollow the same person multiple times.

Task: Given a parameter date D (inclusive), return all active follow relationships at the end of day D (i.e., after all events with event_ts <= D 23:59:59).

Output columns:

  • as_of_date (value = D), follower_id, followee_id

Clarifications/edge cases to handle:

  • If the latest event on or before D is follow, the relationship is active.
  • If the latest event is unfollow, it is inactive.
  • If there are no events on/before D, it should not appear.

Part B — Add reciprocal friendships (SQL)

You have a directed friendship table:

  • friendships(user_id, friend_id)

A friendship should be bidirectional (if (A,B) exists, (B,A) should also exist).

Task: Produce the set of missing reciprocal rows that must be inserted to make the data bidirectional, without creating duplicates.

Output columns: user_id, friend_id representing rows to insert.

Part C — Mutual friends (Python)

You are given an undirected friendship list edges, where each element is a pair (u, v) meaning u and v are friends.

Task: Implement a function that returns the set (or sorted list) of mutual friends of two users a and b.

Constraints to consider:

  • The graph can be large; aim for an efficient approach.

Part D — Valid car bookings (Python)

A single car can be booked by only one customer at a time.

You are given booking requests as a list of tuples (booking_id, start_time, end_time), where start_time < end_time.

Rule: Accept bookings in increasing start_time order; a booking is valid/accepted if it does not overlap with any previously accepted booking.

Task: Return the list of accepted booking_ids.

Edge cases:

  • Back-to-back bookings where end_time == next_start_time do not overlap.
  • Input may be unsorted.
Solution
Analytics & Experimentation
7.

Define and analyze product metrics

MediumAnalytics & Experimentation

Product Analytics Case: Short‑Form Video Feed

Context: You are evaluating a short‑form video feed feature inside a large social app where users swipe through algorithmically ranked videos. The goal is to maximize high‑quality watch time while maintaining healthy creator supply and monetization.

Tasks

  1. Define 4–6 success metrics to evaluate the feature.
  2. If the primary metric suddenly drops, outline a step‑by‑step root‑cause investigation plan.
  3. Identify one leading metric you would monitor and justify why it predicts the primary outcome.
  4. List key business questions stakeholders will ask and how you would answer them analytically.
  5. Propose meaningful user segments to slice the metrics and explain what each segment could reveal.
Solution
8.

Define success metrics for a social feed

MediumAnalytics & Experimentation

Define Success Metrics for a Social Feed Feature

You are evaluating a change to the main social feed in a large-scale consumer app. Assume events are logged to a single analytics table with impression-level and action-level events.

Minimal events table (to ground definitions):

  • user_id, session_id, event_time (UTC), tz_offset_min
  • event_type ∈ {feed_load, impression, like, comment, share, save, hide, report, follow, unfollow, crash}
  • impression_id, content_id, creator_id
  • dwell_ms (on impression), server_latency_ms (on feed_load)
  • device_integrity ∈ {PASS, FAIL}, bot_score ∈ [0,1]

Specify:

(a) The primary success metric(s) with exact formulas (numerator/denominator and units).

(b) Guardrail metrics (e.g., latency, content quality, reliability) with formulas and units.

(c) The eligible population and unit of analysis (e.g., user-day, session, impression) for the primary metric and guardrails.

(d) Data hygiene rules: how to handle bots, empty sessions, outliers, and time zones.

(e) How each metric would be computed from the events table (outline the steps and any key joins/windowing).

Finally, discuss the trade-offs between engagement, quality, and reliability, and how your metric set balances these.

Solution
Behavioral & Leadership
9.

Demonstrate ownership and conflict resolution

MediumBehavioral & Leadership

Behavioral: 0→1 Data Initiative, Prioritization, and Cross-Functional Leadership

Context: Onsite interview for a Data Engineer role. Provide a concise, structured (STAR) answer that uses concrete metrics and impact.

Prompt

  1. Describe a time you took a data initiative from zero to one.
    • What problem did you target, who were the stakeholders, and what constraints or scale did you face?
  2. How did you prioritize when responsibilities or requests conflicted?
    • Which framework or criteria did you use, and why?
  3. How did you coordinate with product, engineering, and analytics?
    • What rituals, artifacts, or agreements kept everyone aligned?
  4. Give a specific example of a conflict you resolved.
    • What options did you consider, what decision did you make, what trade-offs were involved, and what was the impact (with metrics)?
  5. What are your expectations and plans for your next role?
    • What will you optimize for, and how will you measure success?
Solution
10.

Answer DE behavioral and ramp-up questions

MediumBehavioral & Leadership

Answer the following behavioral questions for a Data Engineer (or data-focused full-stack) role. Provide specific examples.

  1. Project under a tight deadline: Tell me about a project you delivered with limited time.
  2. Conflict: Describe a time you had a conflict with a stakeholder/teammate. What did you do and what was the outcome?
  3. Ramp-up plan: If you joined this team, what would you do in your first 3 months and first 6 months?

Interviewers may ask follow-ups to probe depth (scope, tradeoffs, impact, and what you’d do differently).

Solution

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About the Interview Process

What to expect

Meta's 2026 Data Engineer loop is more SQL-centric and product-aware than a typical data engineering interview. The emphasis is on practical analytics engineering: writing business-facing SQL under time pressure, designing reliable datasets and pipelines, and showing that you understand how data work shapes product decisions. Compared with a general software role, Meta weights advanced SQL, data modeling, metrics judgment, and ownership more heavily than algorithmic problem-solving.

A typical path runs recruiter screen → technical screen → virtual onsite → hiring committee review → team matching → offer. The full process commonly takes 4 to 8 weeks, though it can run longer because committee review and team matching are not always fast.

Interview process

Recruiter screen

Usually a short phone or video call (roughly 25 to 45 minutes). The recruiter checks baseline fit, your relevant data engineering background, communication, and motivation, and covers practical details like leveling, location, and compensation expectations. Expect a resume walkthrough, discussion of recent projects, and a few light behavioral prompts about teamwork or conflict.

Technical screen

Typically a 60-minute live coding session, often split into roughly equal SQL and Python (or general coding) portions plus a brief intro and wrap-up. The focus is SQL fluency, coding fundamentals, and how clearly you reason through edge cases and ambiguity. Be ready for joins, aggregations, subqueries, window functions, and ranking on the SQL side, and basic Python transformations using common data structures on the coding side.

Virtual onsite (full loop)

The onsite usually runs about 4 to 5 hours and consists of 4 to 5 back-to-back interviews, commonly 45 minutes each with short breaks. A typical mix includes:

  • SQL / ETL — query writing and data transformation
  • Data modeling or pipeline design — schema design and reliable data flow
  • Product sense / metrics — defining and diagnosing product metrics
  • Behavioral / Ownership — impact, conflict, and how you operate

Some teams add a fifth round for deeper technical or level-specific evaluation. The exact composition varies by team and level.

Hiring committee review

After the onsite, Meta generally relies on a committee-based review rather than a single interviewer's verdict. The committee weighs the full signal across rounds, the consistency of your strengths, and your likely level. You don't participate directly, and this stage is one reason timelines become harder to predict after the onsite.

Team matching

Team matching at Meta often happens after you clear the hiring bar rather than before. Your interviews generally assess whether you meet the general standard for the role, not just fit for one specific team. This can add waiting time even after a positive interview outcome.

Offer

If committee review and team matching both go well, the process closes with an offer discussion covering level, scope, and role fit. Timing varies, especially when matching is slow or several teams are in consideration.

What they test

Meta's Data Engineer interview leans heavily on SQL, data modeling, and practical analytics engineering. The strongest signal comes from SQL.

SQL (the core)

Be fluent with:

  • Joins and self-joins, GROUP BY / HAVING, subqueries, and CTEs
  • CASE expressions, deduplication, NULL handling, and date logic
  • Window functions — especially worth extra preparation

Most problems are business-style rather than textbook SQL: retention, funnel, cohort, ranking, top-N, and event-log analysis. Expect to state your assumptions and walk through edge cases as you go, not just produce a correct query.

Python (secondary)

Python matters, but usually as a supporting skill. Think strings, lists, dicts, parsing, aggregation, and clean transforms — clear reasoning over heavy algorithmic difficulty.

Data modeling and pipeline design

The onsite goes beyond query writing. Be ready to discuss:

  • Fact and dimension modeling; star vs. snowflake tradeoffs
  • Normalization vs. denormalization; table grain and partitioning
  • Slowly changing dimensions, schema evolution, and backfills
  • Data quality checks, reliability, and batch vs. streaming decisions

Product and metrics judgment

Meta puts real weight on product thinking: defining metrics for products and features, diagnosing why a metric moved, spotting instrumentation gaps, and reasoning about DAU/MAU, retention, conversion, and engagement. Connect your engineering choices to product speed, data completeness, and business outcomes.

Behavioral

The bar is high for ownership, moving fast under ambiguity, direct communication, cross-functional influence, and measurable impact beyond your immediate tasks.

How to prepare

  1. Prioritize advanced business SQL over generic coding practice. Drill messy problems with event logs, deduplication, retention, funnels, and performance-aware query rewrites until they feel routine.
  2. Narrate before you write. Interviewers want to hear how you decompose an ambiguous question, state assumptions, handle NULLs and edge cases, and choose the right grain or join strategy.
  3. Bring product thinking into every technical answer. For any pipeline, table, or metric, explain who uses it, what decision it supports, and the tradeoffs you're making on freshness, accuracy, and cost.
  4. Prepare distinct behavioral stories. Have separate examples for ownership, conflict, moving fast, a mistake, and cross-functional influence. Reusing one story across the loop is a common weakness that makes your experience sound shallow.
  5. Quantify your impact. Lead with what changed — latency reduced, data quality improved, analyst time saved, experiments unblocked, decisions enabled — not the tasks you performed.
  6. Get comfortable with imperfect information. Show how you scope a problem, commit to a path, and correct course rather than waiting for complete clarity.
  7. Know Meta's products well enough to talk metrics naturally. Be ready to discuss engagement, retention, conversion, and instrumentation for products like Facebook, Instagram, WhatsApp, or Threads in concrete terms.

Frequently Asked Questions

It is tough, but not impossible if you prepare the right way. When I went through it, the bar felt high on SQL, data modeling, and communication more than on flashy algorithm tricks. Meta wants people who can reason clearly about large-scale data systems and explain tradeoffs without rambling. The hardest part is that interviewers move fast and expect structured answers. If your fundamentals are solid and you have practiced under time pressure, it feels demanding but fair rather than random.

The process usually starts with a recruiter conversation, then a phone or screening round. After that comes the onsite or virtual onsite, which is where most of the evaluation happens. In my experience, the main rounds focused on SQL, data modeling, product or metrics sense, and behavioral questions. Some loops also include coding with Python or another language, depending on the team. The exact mix can shift a bit, but you should expect several back-to-back interviews testing both technical judgment and how you work with others.

For most people, I would say four to eight weeks of focused prep is enough if you already use SQL and data systems at work. That was the sweet spot for me. If you are rusty on joins, window functions, schema design, or writing clean query logic, give yourself closer to two months. If you are already strong, three to four weeks can work. The key is not just reading notes. You need timed practice, mock interviews, and repetition until your answers come out clean and organized.

SQL is the center of gravity, especially joins, aggregations, window functions, subqueries, CTEs, and performance tradeoffs. Data modeling matters a lot too, including fact and dimension thinking, event schemas, partitions, and handling messy real-world requirements. I also saw a lot of emphasis on metrics definition, product thinking, and being able to explain how you would build trustworthy pipelines. Behavioral prep matters more than people think because Meta cares about ownership, influence, and working through ambiguity. Basic coding helps, but usually it is not the main thing.

The biggest mistake is jumping into an answer without clarifying the problem. I saw people lose points by writing SQL too fast, missing edge cases, or not checking assumptions. Another common issue is weak communication: good ideas delivered in a messy way do not land well. Candidates also get hurt by overfocusing on LeetCode-style prep and ignoring data modeling and metrics. On behavioral questions, vague stories are a problem. You need specific examples with your role, the tradeoffs, what changed, and what you learned.

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