Reddit Data Analyst Interview Guide

Reddit's advertising revenue is virtually the entire business, and it's growing fast. That single fact shapes everything about this data analyst role: you're not supporting the revenue engine from a distance, you're inside it, building the dashboards and experiment readouts that determine how ads get placed, priced, and optimized across the platform.

Reddit Data Analyst Role

Your first year will likely be spent embedded in a product pod like Ads Analytics or Community Growth, owning the metric definitions that product managers and sales leaders reference daily. Success looks like having built a dashboard in a tool like Looker that leadership actually opens every Monday, designed A/B tests that influenced ship decisions, and fixed at least one data quality issue in the warehouse that was silently distorting a KPI. The job rewards the person who can say "that number is wrong, here's why, and here's what we should do instead."

A Typical Week

Writing and communication eat a surprisingly large share of the week, nearly rivaling meetings. Reddit analysts draft experiment summaries that circulate to senior ads leadership before ship decisions get made, and they document metric definitions on the team wiki to stop PMs from using terms like "qualified conversion" and "engaged impression" interchangeably. If you pictured this role as heads-down SQL from 9 to 5, recalibrate: the analysis work is the foundation, but the writing and presenting around it is where you earn influence.

Projects & Impact Areas

Ad revenue optimization sits at the center. You'll trace users through the conversion funnel from promoted post impressions through clicks to purchase actions, segmenting by subreddit category and user tenure, then help the Ads Growth team weigh whether a new placement justifies the UX cost. That work connects directly to community health analysis, because Reddit can't sell ads against subreddits with declining engagement or toxic dynamics. Analysts explore signals like content creation vs. lurking ratios and moderation action impacts, feeding insights into both the ads targeting pipeline and product teams building features like Reddit's AI-powered shopping search.

Skills & What's Expected

The underrated skill here is data visualization and storytelling, which Reddit weights on par with statistics. You're presenting experiment readouts to rooms of 8+ people from product, engineering, and design, and they will push back hard on whether your revenue lift justifies the user experience tradeoff. The overrated worry for this specific role? Spending prep time on ML frameworks or infrastructure tooling. Reddit's job descriptions and interview loops for analysts focus on translating messy community behavior into clean business narratives, not on building models or managing pipelines.

Levels & Career Growth

From what candidates report, mid-level analysts execute within a single product pod, owning specific dashboards and experiment readouts, while senior analysts own metric frameworks that span multiple teams and shape roadmap prioritization. The gap between those levels isn't SQL complexity. It's whether you proactively build measurement infrastructure your pod didn't know it needed, versus staying reactive to the ad-hoc request queue. Growth paths can fork toward analytics engineering (more pipeline and modeling work) or toward strategic product analytics, depending on whether you lean technical or business-side.

Work Culture

Reddit describes itself as distributed-first, though some teams (particularly SF-based ones) operate on a hybrid model with in-office days Tuesday through Thursday. The pace is steady without being brutal, with most analysts working roughly 9:30 to 6 and only occasional evening Slack. The data org is lean relative to headcount, so you get more ownership earlier than you would at Meta or Google, but that also means more context-switching between ad-hoc requests and longer-term projects.

Reddit Data Analyst Compensation

Reddit's RSU grants are liquid stock now, which means your total comp fluctuates with the share price every quarter. The RSU grant size is your primary negotiation lever. Reddit operates under a structured compensation philosophy where base salary bands are tight, but equity grants carry more flexibility, especially for candidates who bring competing offers or a well-researched total comp target.

Because Reddit's comp structure treats base and RSUs as the two main negotiable components, don't burn your negotiation energy trying to push base salary beyond the band. Instead, anchor on total comp and make your case for a larger equity grant. Reddit's ad-driven revenue model (advertising accounts for virtually all of the company's income) gives you a concrete framing: tie your expected impact to the ads or community growth metrics that actually move the business.

Reddit Data Analyst Interview Process

Plan for about six weeks end to end across six rounds. The take-home assignment (budgeted at roughly six hours of work) and subsequent panel presentation are the final gate, and they're where Reddit's process diverges from the standard data analyst loop. Your presentation needs to land a specific recommendation tied to Reddit's business, like how a metric change would affect ad auction performance or subreddit engagement quality, not just a walkthrough of your exploratory analysis.

The hiring manager screen in round two is more substantive than a typical fit conversation. Reddit's HM round explicitly covers product sense alongside behavioral questions, probing whether you can connect analytical work to decisions about things like feed ranking or advertiser retention. If you can't demonstrate that kind of business acumen early, strong SQL chops in later rounds may not be enough to compensate.

Reddit Data Analyst Interview Questions

WITH signups AS (
  SELECT
    u.user_id,
    DATE(u.signup_ts) AS signup_date
  FROM users u
),
retained_flag AS (
  SELECT
    s.user_id,
    s.signup_date,
    /* Retained if at least one session occurs on day 1 through day 7 after signup */
    MAX(
      CASE
        WHEN DATE_DIFF(DATE(se.session_ts), s.signup_date, DAY) BETWEEN 1 AND 7 THEN 1
        ELSE 0
      END
    ) AS is_retained_7d
  FROM signups s
  LEFT JOIN sessions se
    ON se.user_id = s.user_id
   /* Optional pushdown to reduce scan while preserving correctness */
   AND DATE(se.session_ts) BETWEEN s.signup_date + INTERVAL 1 DAY AND s.signup_date + INTERVAL 7 DAY
  GROUP BY 1, 2
)
SELECT
  signup_date,
  COUNT(*) AS new_users,
  SUM(is_retained_7d) AS retained_users_7d,
  SAFE_DIVIDE(SUM(is_retained_7d), COUNT(*)) AS retention_rate_7d
FROM retained_flag
GROUP BY 1
ORDER BY 1;

WITH base AS (
  SELECT
    subreddit_id,
    user_id,
    DATE(created_ts) AS comment_date,
    created_ts
  FROM comments
),
first_comment AS (
  SELECT
    subreddit_id,
    user_id,
    MIN(comment_date) AS first_comment_date
  FROM base
  GROUP BY 1, 2
),
daily_authors AS (
  /* Distinct authors per subreddit per day */
  SELECT DISTINCT
    b.subreddit_id,
    b.comment_date,
    b.user_id
  FROM base b
)
SELECT
  da.subreddit_id,
  da.comment_date,
  COUNT(DISTINCT da.user_id) AS distinct_comment_authors,
  COUNT(DISTINCT CASE WHEN fc.first_comment_date = da.comment_date THEN da.user_id END) AS first_time_authors,
  SAFE_DIVIDE(
    COUNT(DISTINCT CASE WHEN fc.first_comment_date = da.comment_date THEN da.user_id END),
    COUNT(DISTINCT da.user_id)
  ) AS first_time_author_share
FROM daily_authors da
JOIN first_comment fc
  ON fc.subreddit_id = da.subreddit_id
 AND fc.user_id = da.user_id
GROUP BY 1, 2
ORDER BY 1, 2;

What jumps out isn't any single area but how experimentation and statistics questions compound on each other, often within the same problem. A question about testing a new Home feed ranking model quickly becomes a statistics question about handling right-skewed session data or correcting for multiple comparisons across 12 UI tweaks, and Reddit's panel expects you to move fluidly between designing the experiment and defending the math. The biggest prep mistake you can make is treating SQL as the main event and cramming everything else the week before, when the real differentiator is whether you can connect a metric design choice to an experiment structure to a statistical justification in one coherent answer.

Practice Reddit-style questions across all six areas at datainterview.com/questions.

How to Prepare for Reddit Data Analyst Interviews

Reddit's full-year 2025 revenue reached roughly $2.2B, up nearly 70% year-over-year, with advertising as the only business segment the company reports. That concentration means data analysts here are tightly coupled to the ads engine: personalized product recommendations, ad targeting, and the new AI-powered shopping search all need metric frameworks, experiment designs, and performance readouts.

At around 2,555 employees, the data org is small enough that you'll own whole problem spaces rather than narrow slices. Reddit's values page explicitly calls out "Remember the Human", and that tension between protecting community experience and scaling ad revenue is the most Reddit-specific thing you can speak to in a "why here" answer. Don't just say you love browsing r/nba. Instead, describe how you'd design guardrail metrics for the AI shopping search rollout that track whether surfacing product recommendations changes how users engage with organic subreddit content. That framing shows you understand what makes Reddit's data problems distinct from, say, Instagram's feed optimization.

Try a Real Interview Question

| experiment_assignments | (table)
|------------------------|
| user_id | experiment_id | variant   | assigned_at |
|--------:|--------------:|-----------|-------------|
| 101     | 42            | control   | 2024-01-01  |
| 102     | 42            | treatment | 2024-01-01  |
| 103     | 42            | treatment | 2024-01-02  |
| 104     | 42            | control   | 2024-01-02  |

| user_sessions | (table)
|---------------|
| user_id | session_id | session_date |
|--------:|-----------:|--------------|
| 101     | 9001       | 2024-01-01   |
| 102     | 9002       | 2024-01-01   |
| 103     | 9003       | 2024-01-02   |
| 102     | 9004       | 2024-01-03   |

-- Task: write a query that outputs daily SRM flag and DAU rates by variant for experiment_id = 42.

WITH days AS (
  SELECT DISTINCT session_date AS day
  FROM user_sessions
),
assigned AS (
  SELECT user_id, variant, assigned_at
  FROM experiment_assignments
  WHERE experiment_id = 42
),
day_population AS (
  -- users considered "in" on a given day if assigned_at <= day
  SELECT
    d.day,
    a.user_id,
    a.variant
  FROM days d
  JOIN assigned a
    ON a.assigned_at <= d.day
),
day_activity AS (
  SELECT
    session_date AS day,
    user_id,
    1 AS is_active
  FROM user_sessions
  GROUP BY 1, 2
),
day_metrics AS (
  SELECT
    p.day,
    COUNT(*) AS assigned_users,
    SUM(CASE WHEN p.variant = 'treatment' THEN 1 ELSE 0 END) AS assigned_treatment,
    SUM(CASE WHEN p.variant = 'control' THEN 1 ELSE 0 END) AS assigned_control,
    SUM(CASE WHEN p.variant = 'treatment' AND COALESCE(act.is_active, 0) = 1 THEN 1 ELSE 0 END) AS active_treatment,
    SUM(CASE WHEN p.variant = 'control' AND COALESCE(act.is_active, 0) = 1 THEN 1 ELSE 0 END) AS active_control
  FROM day_population p
  LEFT JOIN day_activity act
    ON act.day = p.day
   AND act.user_id = p.user_id
  GROUP BY 1
)
SELECT
  day,
  assigned_users,
  CAST(assigned_treatment AS DECIMAL(18,6)) / NULLIF(assigned_users, 0) AS treatment_share,
  CASE
    WHEN ABS(CAST(assigned_treatment AS DECIMAL(18,6)) / NULLIF(assigned_users, 0) - 0.5) > 0.02 THEN 1
    ELSE 0
  END AS srm_flag,
  CAST(active_control AS DECIMAL(18,6)) / NULLIF(assigned_control, 0) AS control_dau_rate,
  CAST(active_treatment AS DECIMAL(18,6)) / NULLIF(assigned_treatment, 0) AS treatment_dau_rate,
  (CAST(active_treatment AS DECIMAL(18,6)) / NULLIF(assigned_treatment, 0))
  - (CAST(active_control AS DECIMAL(18,6)) / NULLIF(assigned_control, 0)) AS abs_lift
FROM day_metrics
ORDER BY day;

Problems involving community engagement data, like aggregating activity across subreddits or joining content tables with interaction logs, are a good proxy for the kind of SQL you'll face. Reddit's platform structure (nested comments, 100K+ active communities, overlapping user bases) naturally produces queries that test window functions and self-joins more than simple aggregations. Sharpen these patterns at datainterview.com/coding.

Test Your Readiness

Run through product sense and experimentation questions at datainterview.com/questions, focusing on metric design for platforms where community health and ad revenue can pull in opposite directions.