Meta Data Analyst Interview Guide

From hundreds of mock interviews, one pattern keeps showing up: candidates prep for Meta's Data Analyst role like it's a SQL test with some behavioral questions bolted on. It's not. The real filter is whether you can turn messy workforce data into a narrative that changes how a VP thinks about headcount, retention, or recruiting strategy. SQL mastery and stats are table stakes. The differentiator is storytelling at expert level, paired with a deep understanding of how people data connects to business outcomes.

Meta Data Analyst Role

This is a People Analytics role, not a product analytics or BI reporting seat. You're embedded with HR, recruiting, and workforce planning teams, answering questions like why engineering attrition spiked in a specific org, whether a new interview process actually improved offer-accept rates, or how to forecast headcount needs for a growing business unit. Success after year one means you've owned at least one analysis that directly changed a people-related decision (a policy shift, a recruiting pipeline fix, a retention intervention) and your manager can point to it as demonstrable impact during performance review.

A Typical Week

The thing that surprises most candidates isn't the analysis time. It's how much of the week goes to writing: decks, internal notes, experiment readouts. You're crafting narratives for an audience of HR leaders and execs who won't read your SQL, so the quality of your written communication matters as much as the query behind it. The other quiet time sink is infrastructure work, fixing broken ETL jobs and backfilling tables when an upstream schema change cascades through your dashboards overnight.

Projects & Impact Areas

Experiment analysis anchors the work, like designing and evaluating A/B tests on recruiting outreach strategies or onboarding program changes, then segmenting results by role family, geography, or tenure cohort to catch effects the topline numbers hide. You're also building and maintaining metric definitions for workforce health (attrition risk scores, time-to-fill, diversity pipeline metrics) and evolving them as the business changes. NLP-based text analysis shows up too: applying social listening and topic tagging techniques to internal survey responses or candidate feedback to surface themes that manual review would miss.

Skills & What's Expected

Writing is the most underrated skill for this role, not SQL. The skill profile rates communication at expert level, which in practice means your Hive query can be slightly rough, but your readout deck needs a clear recommendation with a strong point of view. Data architecture and pipeline skills matter more than you'd guess from the "Data Analyst" title: you're expected to build and maintain ETL processes, enforce data integrity, and model relational data at scale. Machine learning knowledge is about evaluating NLP outputs and topic classifiers, not building models from scratch.

Levels & Career Growth

The blocker at the mid-to-senior transition is almost always scope. Solid individual analysis earns you trust, but promotion requires demonstrating that your work influenced decisions beyond your immediate team, like shaping a recruiting strategy across multiple orgs or redefining a metric that other analysts adopted. At staff level and above, you're setting the analytical vision for an entire people domain and mentoring other analysts. Very few reach principal; those who do operate as de facto heads of analytics for a major business segment.

Work Culture

Meta requires three days in-office per week (Tuesday through Thursday for most teams), and the culture rewards speed over perfection. Analysts are expected to own narratives and drive decisions, not just answer questions, and performance reviews reflect that bias. Code ownership is collective: you'll peer-review teammates' SQL, contribute to shared query libraries, and openly challenge metric definitions, which can feel confrontational if you're coming from a siloed environment.

Meta Data Analyst Compensation

Meta's RSUs vest quarterly at 6.25% over four years, meaning equity hits your brokerage account every three months rather than in annual chunks. The biggest gotcha is that META's stock price at each vest date determines your actual payout, and since equity makes up a larger share of total comp at E5 and above, senior analysts feel that volatility more acutely than juniors whose packages lean heavier on base salary.

RSU grants are, from what candidates report, the most negotiable component of a Meta offer. Base salary bands tend to leave less room. If you're at the E5 level or above with a competing offer from a peer company, push hard on the initial RSU grant size, since that number compounds across four years of vesting. For E3 and E4 candidates with less leverage, focusing negotiation energy on the sign-on bonus can bridge early-tenure comp while your equity vests.

Meta Data Analyst Interview Process

The full loop stretches about 6 weeks, but the real bottleneck is scheduling, not evaluation. From what candidates report, the rounds labeled "Onsite" (rounds 6 and 7) still run as video calls, so don't assume you'll be flying to Menlo Park. Weak product sense is among the most common reasons candidates wash out, even those who nail the SQL and stats rounds. You can write a flawless CTE and still get dinged because you couldn't articulate what a PM should actually do with the result.

Every interviewer writes independent feedback that gets weighed alongside the other rounds, and the hiring manager screen in round 7 carries real decision weight. Silence while you solve a problem doesn't read as "thinking" in written notes; it reads as "stuck." Verbalizing your reasoning isn't just good practice at Meta. It's the only way your signal makes it onto the page that determines whether you advance.

Meta Data Analyst Interview Questions

WITH daily_signups AS (
  -- First, count the number of new users for each day
  SELECT
    CAST(signup_timestamp AS DATE) AS signup_date,
    COUNT(DISTINCT user_id) AS new_users
  FROM user_signups
  WHERE signup_timestamp >= '2024-05-01' AND signup_timestamp < '2024-06-01'
  GROUP BY 1
)
-- Now, calculate the 7-day rolling average
SELECT
  signup_date,
  new_users,
  AVG(new_users) OVER (
    ORDER BY signup_date
    ROWS BETWEEN 6 PRECEDING AND CURRENT ROW
  ) AS rolling_avg_7_day
FROM daily_signups
ORDER BY signup_date;

-- Calculate the overall friend request acceptance rate
SELECT
    -- Sum up all accepted requests and divide by the total number of requests
    -- Casting one of the counts to a float or numeric type is crucial for non-integer division
    CAST(SUM(CASE WHEN status = 'accepted' THEN 1 ELSE 0 END) AS REAL) / COUNT(*) AS acceptance_rate
FROM friend_requests;

WITH active_users_on_day AS (
    -- First, get the distinct list of users who sent a message on the target date
    SELECT DISTINCT sender_id
    FROM messages
    WHERE CAST(sent_timestamp AS DATE) = '2024-06-01'
)
-- Now, for each of these users, find their first-ever message and their last message on that day
SELECT
    m.sender_id,
    -- Use a window function to find the first message timestamp for each user across all their history
    MIN(m.sent_timestamp) OVER (PARTITION BY m.sender_id) AS first_ever_message_ts,
    -- Use another window function, but this time filter for the specific day to get the last message
    MAX(CASE WHEN CAST(m.sent_timestamp AS DATE) = '2024-06-01' THEN m.sent_timestamp ELSE NULL END) OVER (PARTITION BY m.sender_id) AS last_message_on_day_ts
FROM messages m
-- Join back to the main table to get all messages for the active users
JOIN active_users_on_day a ON m.sender_id = a.sender_id
GROUP BY 1, 2, 3 -- Group to get one row per user
ORDER BY 1;

The distribution is lopsided toward judgment calls, not technical execution. Product Sense, Stats, and Behavioral together outweigh pure SQL and data modeling, which means Meta wants analysts who can decide what to measure for something like Facebook Marketplace seller quality, not just write the query that computes it. The biggest prep mistake? Over-rotating on SQL drills while treating stats and product intuition as things you'll "figure out in the moment."

Practice questions across all six areas at datainterview.com/questions.

How to Prepare for Meta Data Analyst Interviews

Meta's full-year 2025 revenue reached $200.97 billion, up 22% year-over-year. Almost all of that comes from advertising, and the 2026 roadmap doubles down on AI-powered ad performance to keep that engine growing. For analysts, this means the highest-priority work often ties back to quantifying how AI model changes move ad revenue and engagement metrics.

On the other side of the business, Reality Labs is splitting the Quest VR platform from Worlds and shifting Worlds to a mobile-first strategy. That creates greenfield analytics work: defining engagement and monetization metrics for a platform that doesn't have established baselines yet. If you're interviewing now, your product sense answers should reflect awareness of both the ad-revenue core and these newer bets.

Your "why Meta" answer needs to reference a specific analytical problem tied to something the company is actively building. Vague enthusiasm about social connection or the metaverse won't register. A stronger angle: point to the Worlds mobile expansion and talk about the challenge of building a measurement framework for a product where user behavior patterns haven't stabilized, then connect that to your own experience defining metrics in ambiguous environments. That shows you've read beyond the headline and understand what the analyst work actually looks like.

Try a Real Interview Question

Table: flags
| flag_id | post_id | flag_dt    |
|---------|---------|------------|
| 1       | 101     | 2023-10-01 |
| 2       | 101     | 2023-10-01 |
| 3       | 102     | 2023-10-01 |
| 4       | 104     | 2023-10-02 |
| 5       | 104     | 2023-10-02 |
| 6       | 103     | 2023-10-02 |
| 7       | 104     | 2023-10-02 |

Table: post_tags
| post_id | tag       |
|---------|-----------|
| 101     | news      |
| 101     | politics  |
| 102     | sports    |
| 102     | news      |
| 103     | tech      |
| 104     | politics  |
| 104     | finance   |

WITH daily_tag_flags AS (
    SELECT
        f.flag_dt,
        pt.tag,
        COUNT(f.flag_id) AS flag_count
    FROM flags f
    JOIN post_tags pt ON f.post_id = pt.post_id
    GROUP BY 1, 2
),
ranked_daily_tags AS (
    SELECT 
        flag_dt,
        tag,
        flag_count,
        RANK() OVER (PARTITION BY flag_dt ORDER BY flag_count DESC) as rnk
    FROM daily_tag_flags
)
SELECT 
    flag_dt,
    tag,
    flag_count
FROM ranked_daily_tags
WHERE rnk <= 3
ORDER BY flag_dt, flag_count DESC;

Meta's SQL round leans on its social-media data model, so you'll encounter schemas built around user-level events, content interactions, and ad impressions across Facebook, Instagram, and Messenger. The internal stack runs on Presto and Hive over petabyte-scale tables, and interviewers design questions that reward careful thinking about joins at that scale (like handling users who exist in one event stream but not another). Practice on similar schemas at datainterview.com/coding.

Test Your Readiness

Identify your weak spots before the loop. Run through practice questions at datainterview.com/questions, weighting your time toward product sense and SQL since each carries 25% of question weight.