LinkedIn Data Analyst Interview Guide

LinkedIn posts many of its analytics roles under the title "Business Analyst," which trips up candidates who expect requirements gathering and process mapping. These are full product analytics positions: SQL, experiment readouts, metric frameworks, and stakeholder presentations. If you're searching for comp data or open reqs, look under both "Business Analyst" and "Data Analyst" to get the complete picture.

LinkedIn Data Analyst Role

You'll sit inside a specific product or business unit (Feed Relevance, Trust & Safety, Talent Solutions, or Premium Subscriptions) and own the metrics for that area. Success after year one looks like this: your stakeholders have stopped scrambling to explain metric movements because your reporting already surfaced the answer, and you've shipped at least one analysis that shifted a product decision, like whether to expand a Suggested Posts rollout from 5% to 20%.

A Typical Week

The surprise isn't how much time you spend querying. It's how much time you spend writing: findings docs, metric definition pages, that weekly email your entire pod depends on. Most candidates picture the job as heads-down SQL work, but the reality at LinkedIn is that aligning with a Feed Relevance PM on which question to answer, then packaging results so a VP can forward them to a staff meeting, eats as much of your week as the analysis itself.

Projects & Impact Areas

On the revenue side, you might analyze Premium subscriber retention curves or compare InMail engagement between paid and free members for a VP's quarterly review. Growth work looks different: building cohort analyses for Creator Mode adoption in Snowflake, segmenting creators by follower count and posting frequency to see if the feature actually improves retention or just attracts already-sticky users. Experimentation readouts stitch these threads together, like presenting preliminary data on how a new Suggested Posts module affects session depth so the PM can decide whether to widen the rollout by Friday.

Skills & What's Expected

Business acumen and data visualization score "high" in LinkedIn's skill expectations, while statistics sits at "medium" and engineering skills rank low. That ordering surprises candidates who spend weeks on window functions but zero hours practicing how to define a North Star metric for LinkedIn's job recommendation engine. Medium-level statistics (experiment design, significance testing, knowing when independence assumptions break on a social graph of connected members) matters far more than ML or infrastructure knowledge here.

Levels & Career Growth

The jump from Business Analyst (mid) to Senior is about owning a product area's metrics end-to-end rather than executing analyses someone else scoped. Staff is where the blocker shifts: the promo criteria emphasize cross-org influence, mentoring junior analysts, and defining measurement standards that other teams adopt. According to LinkedIn's own level descriptions, Senior Staff (Principal) requires company-wide impact and is explicitly described as "exceptionally rare."

Work Culture

Based on the day-in-life data, analytics teams cluster their in-office days Tuesday through Thursday at the Sunnyvale campus, with hybrid attendance running two to three days per week. The pace feels measured compared to hypergrowth startups, with culture notes suggesting analysts keep roughly standard hours and see spikes mainly around quarterly business reviews. The flip side of that intentional pace is more process: expect thorough review cycles on analyses and a preference for consensus that can slow down time-sensitive projects.

LinkedIn Data Analyst Compensation

LinkedIn's RSU schedule follows a 4-year structure: nothing vests until the 12-month cliff, then 25% lands at once, with the remainder distributed quarterly. Your cash flow in that first year is base and bonus only, so the annualized TC figure on your offer letter overstates what actually hits your account until that cliff arrives. On LinkedIn's comp ladder, these roles carry the "Business Analyst" title (Business Analyst, Senior Business Analyst, Staff Business Analyst, Senior Staff Business Analyst), so search that title when benchmarking externally or you'll miss relevant data points.

Competing offers are your strongest negotiation lever. The offer notes confirm that base, sign-on bonus, and RSU grants are all negotiable, but a credible competing offer from another established company gives you something concrete to anchor against rather than abstract arguments about market rate. Before the loop even starts, ask your recruiter which level the req is opened for. Getting slotted as a Senior Business Analyst when you have the experience for Staff is a conversation that's far easier to have before interviews than after an offer is already written.

LinkedIn Data Analyst Interview Process

Candidates get rejected most often for answering questions at face value. LinkedIn's interviewers across the Product Sense & Metrics and SQL rounds expect you to challenge the premise, ask what decision the analysis feeds, and connect your work to a specific business lever like Talent Solutions revenue or Premium subscriber retention. Pulling the right number without framing why it matters reads as "data pull monkey," not analyst.

One thing worth knowing: your experience needs to map tightly to the team's domain. Talent Solutions analytics (recruiter tool adoption, job posting fill rates) looks nothing like Marketing Solutions analytics (ad auction efficiency, campaign attribution). Generic stories about "driving insights" without anchoring them in the hiring team's product area tend to produce weak interviewer signal, and from what candidates report, weak signal is treated the same as negative signal when the final decision gets made.

LinkedIn Data Analyst Interview Questions

/*
Assumed tables
- members(member_id, signup_ts)
- profile_photos(member_id, photo_uploaded_ts)
- member_skills(member_id, skill_added_ts)
- company_follows(member_id, company_id, followed_ts)
- connections(requester_id, accepter_id, connected_ts)

Activation definition: within 7 days of signup, complete >= 3 of:
1) upload photo
2) add >= 3 skills
3) follow >= 5 companies
4) connect with >= 3 people
*/
WITH base_members AS (
  SELECT
    m.member_id,
    m.signup_ts,
    DATE_TRUNC('week', m.signup_ts) AS signup_week,
    m.signup_ts + INTERVAL '7 day' AS signup_ts_plus_7d
  FROM members m
  WHERE m.signup_ts >= DATE_TRUNC('week', CURRENT_DATE) - INTERVAL '12 week'
),
photo_flag AS (
  SELECT
    bm.member_id,
    CASE WHEN MIN(pp.photo_uploaded_ts) IS NOT NULL THEN 1 ELSE 0 END AS has_photo_7d
  FROM base_members bm
  LEFT JOIN profile_photos pp
    ON pp.member_id = bm.member_id
   AND pp.photo_uploaded_ts >= bm.signup_ts
   AND pp.photo_uploaded_ts < bm.signup_ts_plus_7d
  GROUP BY bm.member_id
),
skills_flag AS (
  SELECT
    bm.member_id,
    CASE WHEN COUNT(ms.skill_added_ts) >= 3 THEN 1 ELSE 0 END AS has_3_skills_7d
  FROM base_members bm
  LEFT JOIN member_skills ms
    ON ms.member_id = bm.member_id
   AND ms.skill_added_ts >= bm.signup_ts
   AND ms.skill_added_ts < bm.signup_ts_plus_7d
  GROUP BY bm.member_id
),
follows_flag AS (
  SELECT
    bm.member_id,
    CASE WHEN COUNT(DISTINCT cf.company_id) >= 5 THEN 1 ELSE 0 END AS has_5_follows_7d
  FROM base_members bm
  LEFT JOIN company_follows cf
    ON cf.member_id = bm.member_id
   AND cf.followed_ts >= bm.signup_ts
   AND cf.followed_ts < bm.signup_ts_plus_7d
  GROUP BY bm.member_id
),
connections_flag AS (
  SELECT
    bm.member_id,
    CASE WHEN COUNT(*) >= 3 THEN 1 ELSE 0 END AS has_3_connections_7d
  FROM base_members bm
  LEFT JOIN connections c
    ON (c.requester_id = bm.member_id OR c.accepter_id = bm.member_id)
   AND c.connected_ts >= bm.signup_ts
   AND c.connected_ts < bm.signup_ts_plus_7d
  GROUP BY bm.member_id
),
member_level AS (
  SELECT
    bm.member_id,
    bm.signup_week,
    pf.has_photo_7d,
    sf.has_3_skills_7d,
    ff.has_5_follows_7d,
    cf.has_3_connections_7d,
    (pf.has_photo_7d + sf.has_3_skills_7d + ff.has_5_follows_7d + cf.has_3_connections_7d) AS tasks_done_7d
  FROM base_members bm
  JOIN photo_flag pf ON pf.member_id = bm.member_id
  JOIN skills_flag sf ON sf.member_id = bm.member_id
  JOIN follows_flag ff ON ff.member_id = bm.member_id
  JOIN connections_flag cf ON cf.member_id = bm.member_id
)
SELECT
  signup_week,
  COUNT(*) AS signups,
  SUM(CASE WHEN tasks_done_7d >= 3 THEN 1 ELSE 0 END) AS activated_members,
  1.0 * SUM(CASE WHEN tasks_done_7d >= 3 THEN 1 ELSE 0 END) / NULLIF(COUNT(*), 0) AS activation_rate
FROM member_level
GROUP BY 1
ORDER BY 1;

/*
Goal: daily view -> apply_start conversion rate over last 30 days.
Dedupe at (member_id, job_id) within day so repeated views do not inflate views.
Assumed table: events(job_id, member_id, event_name, event_ts)
*/
WITH daily_pair AS (
  SELECT
    DATE_TRUNC('day', e.event_ts) AS event_day,
    e.member_id,
    e.job_id,
    /* Flags after dedupe at the pair level */
    MAX(CASE WHEN e.event_name = 'job_view' THEN 1 ELSE 0 END) AS saw_view,
    MAX(CASE WHEN e.event_name = 'apply_start' THEN 1 ELSE 0 END) AS started_apply
  FROM events e
  WHERE e.event_ts >= DATE_TRUNC('day', CURRENT_DATE) - INTERVAL '30 day'
    AND e.event_name IN ('job_view', 'apply_start')
  GROUP BY 1, 2, 3
),
daily_rollup AS (
  SELECT
    event_day,
    SUM(saw_view) AS deduped_views,
    SUM(CASE WHEN saw_view = 1 AND started_apply = 1 THEN 1 ELSE 0 END) AS deduped_view_to_apply_starts
  FROM daily_pair
  GROUP BY 1
)
SELECT
  event_day,
  deduped_views,
  deduped_view_to_apply_starts,
  1.0 * deduped_view_to_apply_starts / NULLIF(deduped_views, 0) AS view_to_apply_start_cvr
FROM daily_rollup
ORDER BY event_day;

The distribution is surprisingly flat across six areas, which tells you something about how LinkedIn's loop works: no single skill carries you. Candidates who drill SQL in isolation get blindsided when a product sense question about, say, Premium subscriber retention pivots into designing an experiment where treatment and control members share a connection graph. The biggest prep mistake this distribution implies isn't neglecting any one area. It's failing to practice the handoffs between them, because LinkedIn's 1B+ member platform means every metric question quickly bumps into statistical complications and every modeling question demands product context about entities like job postings, InMail threads, or recruiter seats.

Build that cross-area muscle with LinkedIn-relevant prompts at datainterview.com/questions.

How to Prepare for LinkedIn Data Analyst Interviews

LinkedIn's three stated priorities right now are growing Premium subscriptions, expanding revenue streams beyond advertising, and embedding generative AI across the platform. The company sits at roughly $20B in annual revenue with 11% year-over-year growth, and that AI push is visible in everything from the agent-based GenAI stack engineering is building to the Premium subscription features marketing is promoting. For analysts, these bets likely translate into work like measuring AI feature adoption funnels, tracking whether new Premium tiers actually improve retention, and defining success metrics for surfaces that didn't exist a year ago.

Most candidates fumble the "why LinkedIn" question by reciting the mission statement about connecting professionals. That's forgettable. What separates strong answers: reference one of those three company priorities, name a specific measurement challenge it creates (say, how you'd isolate the lift of an AI-powered feature from organic engagement trends), and explain why that problem excites you. LinkedIn's interviewers, from what candidates report, want to hear that you've thought about the analytics problem behind the product, not just that you admire the brand.

Try a Real Interview Question

| experiment_id | member_id | variant   | event_time           |
|---------------|-----------|-----------|----------------------|
| exp_101       | 101       | control   | 2025-01-05 10:00:00  |
| exp_101       | 102       | treatment | 2025-01-05 10:01:00  |
| exp_101       | 103       | control   | 2025-01-05 10:02:00  |
| exp_101       | 104       | treatment | 2025-01-05 10:03:00  |

| experiment_id | member_id | variant   | event_time           |
|---------------|-----------|-----------|----------------------|
| exp_101       | 101       | control   | 2025-01-05 10:05:00  |
| exp_101       | 104       | treatment | 2025-01-05 10:06:00  |
| exp_101       | 104       | treatment | 2025-01-05 10:07:00  |
| exp_101       | 102       | treatment | 2025-01-05 10:08:00  |

-- Write a query that aggregates impressions and clicks per variant,
-- filters variants with at least N=2 exposed members, computes CTR,
-- and computes lift vs the control CTR within the same experiment.

WITH imps AS (
  SELECT
    experiment_id,
    variant,
    member_id,
    COUNT(*) AS imp_cnt
  FROM impression_events
  GROUP BY 1, 2, 3
),
imp_agg AS (
  SELECT
    experiment_id,
    variant,
    COUNT(DISTINCT member_id) AS exposed_members,
    SUM(imp_cnt) AS impressions
  FROM imps
  GROUP BY 1, 2
),
click_agg AS (
  SELECT
    experiment_id,
    variant,
    COUNT(*) AS clicks
  FROM click_events
  GROUP BY 1, 2
),
variant_metrics AS (
  SELECT
    a.experiment_id,
    a.variant,
    a.exposed_members,
    a.impressions,
    COALESCE(c.clicks, 0) AS clicks,
    CAST(COALESCE(c.clicks, 0) AS DECIMAL(18,6)) / NULLIF(a.impressions, 0) AS ctr
  FROM imp_agg a
  LEFT JOIN click_agg c
    ON a.experiment_id = c.experiment_id
   AND a.variant = c.variant
  WHERE a.exposed_members >= 2
),
control AS (
  SELECT
    experiment_id,
    ctr AS control_ctr
  FROM variant_metrics
  WHERE variant = 'control'
)
SELECT
  v.experiment_id,
  v.variant,
  v.exposed_members,
  v.impressions,
  v.clicks,
  v.ctr,
  v.ctr - c.control_ctr AS lift_vs_control
FROM variant_metrics v
JOIN control c
  ON v.experiment_id = c.experiment_id
ORDER BY v.experiment_id, v.variant;

LinkedIn's SQL round, according to candidate reports, leans into queries that reflect the platform's interconnected data: members linked to connections, job postings tied to applications, engagement events spanning multiple surfaces. The problems reward candidates who ask clarifying questions about grain and cardinality before writing a single line. Build that habit by drilling similar multi-table scenarios at datainterview.com/coding.

Test Your Readiness

This quiz covers the specific topic mix LinkedIn weights most (product sense, A/B testing, SQL), so use your results to focus your remaining prep at datainterview.com/questions.