Airbnb Data Analyst Interview Guide

From hundreds of mock interviews, one pattern stands out with Airbnb candidates: they over-prepare for SQL and under-prepare for storytelling. Airbnb's data analysts are expected to craft polished written memos that frame why a metric moved, not just produce the query that found the signal. If you can't translate a booking lift into incremental nights booked and estimated revenue impact, the correct Trino query won't save you.

Airbnb Data Analyst Role

You'll embed directly into a product vertical like Search & Discovery, Trust & Safety, Payments, Host Growth, or Experiences, owning the analytical narrative for that team's KPIs. Not just dashboards. The actual story: why nights booked shifted, whether a Smart Pricing experiment lifted conversion for repeat hosts, what cancellation policy changes did to supply-side retention. Success after year one means your product partners come to you before making roadmap decisions, not after. You're querying Airbnb's internal data platform, likely maintaining pipeline logic in tools like Airflow, building visualizations in Superset, and presenting experiment readouts to VPs who will push back hard on your methodology.

A Typical Week

The surprise in that breakdown isn't the analysis block. It's how much time goes to writing. Most analyst roles at peer companies treat write-ups as an afterthought, but Airbnb's memo culture means you'll spend real hours drafting structured experiment readouts with clear "so what" framing before you ever open a slide deck. The other thing that catches people off guard: a meaningful slice of your week goes to infrastructure work like fixing broken DAGs after upstream schema changes. You're not shielded from pipeline problems here.

Projects & Impact Areas

Experimentation runs through everything. You might spend a quarter validating whether a search ranking weight change interacts with fraud detection signals in Trust & Safety, then pivot to segmenting how flexible payment options shift booking behavior across LATAM and APAC. Analysts on the Host Growth team, meanwhile, design metric frameworks for onboarding funnels and present causal findings that directly determine which features get prioritized next quarter.

Skills & What's Expected

Data modeling intuition and business storytelling are the underrated differentiators. SQL and statistics are non-negotiable (the role requires expert-level SQL and strong stats), but every serious candidate clears that bar. Where people separate themselves is in understanding how booking-listing-user schemas fit together and defending metric choices to a skeptical product lead in plain language. The high scores on business acumen and data visualization in the skill profile aren't decorative. Interviewers will reject candidates who produce correct analyses but can't explain the implications without jargon.

Levels & Career Growth

The widget shows the level bands and comp ranges. What it doesn't show is what actually separates levels. The jump from L4 to L5 hinges on one thing: can you take an ambiguous product question, turn it into a measurable framework, and influence the roadmap without someone telling you what to analyze? L5 to L6 requires cross-team strategic impact, which at a company with only ~6,900 employees means your work needs to visibly move company-level metrics, not just team-level ones.

Work Culture

Airbnb's live-and-work-anywhere policy lets employees work remotely from 170+ countries for up to 90 days per year, though the day-to-day setup from what candidates report is hybrid, with many analysts coming in a few days a week for collaboration energy. The pace is intense but deliberate: Airbnb rewards polished data storytelling over raw speed, so you won't get praised for shipping a sloppy analysis fast. Lean headcount relative to $11B+ in revenue cuts both ways, giving you more direct access to leadership and more ownership than at comparably-sized companies, but also fewer people to delegate to when pipelines break on a Friday afternoon.

Airbnb Data Analyst Compensation

The widget shows the vesting schedule, but the number worth staring at is how much your equity income shrinks each year. By year four, you're vesting less than half of what you received in year one. Stock price movement can soften or worsen that gap, so model your comp using the grant-date value, not a rosy forecast.

Airbnb's most negotiable levers, from what candidates report, are base salary and the initial RSU grant. Having a competing offer from another public tech company gives you real pull on both. Focus your ask on total comp rather than any single component, because Airbnb's recruiters think in TC terms and will rebalance across base and equity to hit a number that works.

Airbnb Data Analyst Interview Process

The full loop runs about four weeks across seven rounds. From what candidates report, the biggest rejection driver is weak SQL skills, not a fumbled behavioral answer. Airbnb's SQL round tests data modeling reasoning on booking-listing-user schemas, not just syntax, and candidates who can't handle cancellation edge cases or tricky date math get cut before they ever reach the later rounds.

Here's what catches people off guard about the decision process: every interviewer writes their feedback independently before anyone discusses the candidate. That structure minimizes anchoring bias, which means a strong impression on the hiring manager can't paper over a mediocre Case Study score where you failed to propose Airbnb-specific metrics like nights booked or guest-to-host conversion. No single round is safe to phone in.

Airbnb Data Analyst Interview Questions

WITH first_search AS (
  SELECT
    si.session_id,
    si.user_id,
    MIN(si.impression_ts) AS first_search_ts,
    DATE(MIN(si.impression_ts)) AS search_day
  FROM search_impressions si
  WHERE si.impression_ts IS NOT NULL
  GROUP BY 1, 2
),
converted_sessions AS (
  SELECT
    fs.session_id,
    fs.search_day,
    1 AS is_converted
  FROM first_search fs
  WHERE EXISTS (
    SELECT 1
    FROM bookings b
    WHERE b.user_id = fs.user_id
      AND b.booking_ts >= fs.first_search_ts
      AND b.booking_ts < fs.first_search_ts + INTERVAL '24' HOUR
      AND b.status IN ('confirmed', 'completed')
  )
)
SELECT
  fs.search_day,
  COUNT(DISTINCT fs.session_id) AS sessions,
  COUNT(DISTINCT cs.session_id) AS converted_sessions,
  CAST(COUNT(DISTINCT cs.session_id) AS DOUBLE) / NULLIF(COUNT(DISTINCT fs.session_id), 0) AS search_to_booking_cvr
FROM first_search fs
LEFT JOIN converted_sessions cs
  ON cs.session_id = fs.session_id
 AND cs.search_day = fs.search_day
GROUP BY 1
ORDER BY 1;

WITH booking_net AS (
  SELECT
    b.booking_id,
    b.listing_id,
    DATE(b.booking_ts) AS booking_day,
    COALESCE(b.gross_amount_usd, 0) - COALESCE(b.refund_amount_usd, 0) AS net_amount_usd
  FROM bookings b
  WHERE b.status IN ('confirmed', 'completed', 'refunded')
),
listing_day AS (
  SELECT
    listing_id,
    booking_day,
    SUM(net_amount_usd) AS net_revenue_usd
  FROM booking_net
  GROUP BY 1, 2
),
scored AS (
  SELECT
    listing_id,
    booking_day,
    SUM(net_revenue_usd) OVER (
      PARTITION BY listing_id
      ORDER BY booking_day
      RANGE BETWEEN INTERVAL '28' DAY PRECEDING AND INTERVAL '1' DAY PRECEDING
    ) AS trailing_28d_net_revenue_excl_today_usd
  FROM listing_day
)
SELECT
  listing_id,
  trailing_28d_net_revenue_excl_today_usd
FROM scored
WHERE booking_day = CURRENT_DATE - INTERVAL '1' DAY
ORDER BY trailing_28d_net_revenue_excl_today_usd DESC NULLS LAST
LIMIT 50;

Airbnb's two-sided marketplace creates a unique evaluation trap: you'll face Product Sense questions that demand host-supply reasoning and Experimentation questions where guest-side randomization leaks into host behavior, and the interviewers expect you to handle both flavors in the same loop. Candidates who drill queries in isolation get blindsided because Airbnb's SQL round itself tests data modeling judgment (choosing the right grain, handling cancellations and partial refunds) rather than syntax recall. If your prep plan doesn't include timed reps on metric trees for Airbnb-specific features like Reserve Now Pay Later and on reasoning through interference in marketplace experiments, you're under-investing in the areas where most rejections actually happen.

Practice Airbnb-tagged questions across all seven areas at datainterview.com/questions.

How to Prepare for Airbnb Data Analyst Interviews

Airbnb has publicly stated its goal of reaching more than 1 billion annual guests by 2028, and the company is placing concrete bets to get there. Reserve Now Pay Later went global to lower booking friction, revenue hit $12.2 billion (up 12% YoY), and headcount grew roughly 12% in the same period. For a data analyst, that context matters because your interviewers will expect you to connect your work back to measurable guest-growth levers, not abstract marketplace theory.

Most candidates fumble the "why Airbnb" question by paraphrasing the mission statement about belonging. What separates strong answers is naming a specific measurement problem you want to own and why it's uniquely hard here. You might talk about how Reserve Now Pay Later changes the cancellation-rate calculus for hosts, or how Airbnb's written-memo culture means your analysis has to stand on its own without a presenter narrating the slides. Show that you've internalized the two-sided marketplace constraint: every metric you'd propose for guests has a shadow effect on hosts.

Try a Real Interview Question

| experiment_assignments |
|------------------------|
| user_id | experiment_name | variant   | assigned_at           | market |
|--------|------------------|-----------|-----------------------|--------|
| 101    | search_ranker_v2  | control   | 2026-01-01 10:00:00   | US     |
| 102    | search_ranker_v2  | treatment | 2026-01-02 09:00:00   | US     |
| 103    | search_ranker_v2  | control   | 2026-01-03 12:00:00   | FR     |
| 104    | search_ranker_v2  | treatment | 2026-01-03 08:30:00   | FR     |

| sessions |
|----------|
| session_id | user_id | session_start         | did_book |
|------------|---------|-----------------------|----------|
| 9001       | 101     | 2026-01-02 11:00:00   | 1        |
| 9002       | 101     | 2026-01-10 09:00:00   | 0        |
| 9003       | 102     | 2026-01-05 14:00:00   | 0        |
| 9004       | 103     | 2026-01-04 13:00:00   | 0        |
| 9005       | 104     | 2026-01-06 07:00:00   | 1        |

-- Write a SQL query that returns market, conv_control, conv_treatment, and lift (treatment minus control)
-- for experiment_name = 'search_ranker_v2'.

WITH base AS (
  SELECT
    a.market,
    a.variant,
    s.session_id,
    CAST(s.did_book AS DOUBLE) AS did_book
  FROM experiment_assignments a
  JOIN sessions s
    ON s.user_id = a.user_id
   AND s.session_start >= a.assigned_at
   AND s.session_start < a.assigned_at + INTERVAL '7' DAY
  WHERE a.experiment_name = 'search_ranker_v2'
), agg AS (
  SELECT
    market,
    variant,
    AVG(did_book) AS conversion_rate
  FROM base
  GROUP BY 1, 2
)
SELECT
  market,
  MAX(CASE WHEN variant = 'control' THEN conversion_rate END) AS conv_control,
  MAX(CASE WHEN variant = 'treatment' THEN conversion_rate END) AS conv_treatment,
  (MAX(CASE WHEN variant = 'treatment' THEN conversion_rate END)
   - MAX(CASE WHEN variant = 'control' THEN conversion_rate END)) AS lift
FROM agg
GROUP BY 1
ORDER BY 1;

Problems like this are representative because Airbnb's data model spans bookings, listings, users, and reviews, all linked by date logic where edge cases (cancellations, currency, seasonality across hemispheres) quietly break naive queries. From what candidates report, readable CTEs and explicit handling of those edge cases matter as much as getting the right answer. Drill similar patterns on datainterview.com/coding.

Test Your Readiness

The quiz above surfaces gaps you might not notice until you're mid-loop. For deeper reps on product sense, experimentation design, and behavioral storytelling calibrated to Airbnb's Bar Raiser format, work through the question sets on datainterview.com/questions.