TikTok SQL Interview Questions: Complete Prep for Data Analysts & Engineers

TikTok runs on data—more than you might imagine. Every swipe, like, video upload, or ad view becomes part of a vast, fast-moving dataset that the platform uses to drive decisions in real time. Whether it’s surfacing trending content or detecting spammy behavior, SQL is at the core of TikTok’s analytics infrastructure. That’s why SQL assessments have become a cornerstone of the TikTok interview process, especially for roles like Data Analyst, Data Scientist, and Data Engineer.

In this guide, we’ll break down the most common TikTok SQL interview questions across experience levels. You’ll learn how to approach TikTok-specific metrics, avoid common pitfalls, and build queries that reflect real-world scenarios—plus get sample answers, mock challenges, and strategic prep tips tailored to TikTok’s ecosystem.

Why SQL Is a Must-Have for TikTok Data Roles

From creator churn analysis to A/B testing new ad formats, TikTok’s data teams are expected to work with billions of rows spanning user behavior, video metadata, and monetization signals. SQL isn’t just a nice-to-have—it’s the language that powers product intelligence, experiment design, and business dashboards.

Candidates who succeed in TikTok’s SQL screens can not only write correct queries, but also reason through edge cases, communicate trade-offs, and optimize for performance. Especially in time-boxed rounds like HackerRank or on-platform SQLPads, fluency in JOINs, window functions, and date logic can make the difference between a pass and a rejection.

TikTok SQL interview questions are designed to evaluate how well candidates can navigate large-scale data tables related to creators, engagement, content, and monetization. Unlike behavioral questions or high-level system design prompts, these focus specifically on writing clean, efficient SQL queries that deliver business-critical insights.

You can expect questions to fall into three broad formats:

Conceptual Questions

These test your understanding of relational logic and foundational syntax. Expect prompts like explaining different types of joins, identifying primary keys, or walking through how a window function works. Conceptual questions are often used early in the TikTok interview process to screen for baseline technical knowledge, especially for intern and entry-level candidates.

Applied Metrics & Business Logic

In this format, you’ll be handed schema for creator activity, video views, or campaign spending—and asked to derive KPIs. For instance: “Find the top 3 most engaging videos per region last week,” or “Calculate average revenue per creator cohort.” These mirror the real work of a TikTok data analyst interview and require both SQL fluency and a good sense of product intuition.

Edge-Case Debugging

These are the trickiest. You’ll get a partially written query—or a data bug—and need to identify what’s wrong and how to fix it. Think: removing double-counting in LEFT JOINs, fixing off-by-one date logic, or optimizing an inefficient query. This type of question is common for 3–5+ year candidates, where TikTok wants to see how you handle ambiguity under time pressure.

At any stage, SQL can appear in take-home assessments, OA rounds (e.g., HackerRank), or live screens. Up next, we’ll share a cheat sheet with patterns, pitfalls, and functions that show up most often.

Before diving into mock questions, it’s worth building a mental framework for what to expect. SQL interviews at TikTok aren’t just about whether your query runs—they test how you approach ambiguous metrics, optimize joins at scale, and reason through content-specific data. This section outlines the most common patterns and pitfalls seen across TikTok SQL interview questions and answers, whether you’re prepping for an analyst screen or a live coding round with an engineer.

Difficulty Tiers

TikTok SQL interviews often follow a clear difficulty progression. Basic questions test your ability to manipulate small datasets, perform simple joins, and apply aggregation functions correctly—think counting active creators or finding top hashtags. Intermediate questions usually introduce multi-table joins, window functions, and performance considerations, such as calculating retention or session frequency. Advanced questions are rare but reserved for senior candidates; they typically involve debugging messy queries, optimizing for compute cost, or detecting anomalies in massive streaming tables.

Understanding where your experience falls on this scale helps you focus your prep time more strategically.

Most-Asked Clauses and Functions

TikTok’s data questions consistently rely on a few critical SQL clauses. Expect to use JOIN (especially LEFT JOIN and INNER JOIN) in most queries. Functions like ROW_NUMBER(), RANK(), and LAG() are staples for cohort tracking or engagement funnels—an essential toolkit for any data role. You’ll also need to be fluent with conditional logic (CASE WHEN), temporal transformations (DATE_TRUNC, EXTRACT, NOW()), and nested subqueries.

Knowing these functions cold can make a big difference when you’re on a timed assessment or navigating a complex prompt.

Top Pitfalls to Avoid

Three common traps show up across data analyst SQL interview questions at TikTok:

1. Off-by-one date filters – A simple BETWEEN clause might accidentally exclude a day if not adjusted properly.
2. Double-counting with LEFT JOIN – Forgetting to deduplicate before aggregating often leads to inflated metrics.
3. Missing NULL handling – TikTok datasets may contain sparse user activity or optional metadata, so failing to coalesce or filter nulls leads to incorrect results.

Recognizing these patterns beforehand helps you debug faster and show thoughtfulness in interviews.

Pro Tip

Before submitting your query—especially in a TikTok HackerRank question or phone screen—always run a sanity check on row counts and join logic. Ask yourself: “Do these numbers make sense given what I expect from the data?” TikTok’s SQL rounds aren’t about perfection—they’re about demonstrating structured thinking under pressure.

Basic TikTok SQL Interview Questions (0–2 Years)

Early-career candidates interviewing at TikTok—especially for Data Analyst or Business Intelligence roles—should expect questions grounded in practical data manipulation. These basic SQL interview questions for data analysts usually revolve around filtering, joining, and summarizing creator activity. Whether you’re pulling top hashtags from a dataset or calculating daily active users, what matters most is clarity, accuracy, and clean logic.

These questions often come from actual TikTok HackerRank questions, used in first-round technical assessments. They test not just syntax fluency, but also your ability to reason through business metrics under time pressure.

1. What is the average quantity of each product purchased per transaction, broken out by year?

   Use YEAR(purchase_date) to bucket each row; then AVG(quantity) in a GROUP BY year, product_id. Round with ROUND(avg_qty, 2) and sort ascending by year → product. Interviewers check that you can mix date extraction, grouping, and numeric formatting in one clean query.

2. Which neighborhoods have zero registered users?

   Perform a LEFT JOIN neighborhoods n ON n.id = u.neighborhood_id and keep rows where u.neighborhood_id IS NULL. This anti-join pattern tests NULL handling and is a fast way to surface “coverage gaps” in TikTok’s city‐level creator outreach.

3. For users who registered in 2022, how much money was spent on each item?

   Join users to purchases, filter registration_date BETWEEN '2022-01-01' AND '2022-12-31', then GROUP BY item_id and sum the amount. The prompt verifies date filtering, basic aggregation, and join correctness.

4. Which videos posted yesterday received zero comments?

   Anti-join the videos table to comments on video_id, filter WHERE video_date = CURRENT_DATE - 1 and comments.video_id IS NULL, then select video_id, creator_id. Demonstrates date filtering plus the “missing child” pattern.

5. List each creator’s total likes in the past seven days and order the list from most to least likes.

   Aggregate SUM(likes) from the video_metrics table where event_date >= CURRENT_DATE - 7, group by creator_id, and sort descending. Checks windowed date filters and simple ranking.

6. For every creator, what is the average video length (in seconds) for 2023?

   Filter video_upload_date to 2023, then AVG(duration_seconds) per creator_id. Ensures candidates can mix numeric averages with year filters.

7. Return the top five hashtags by total views last month.

   Join video_hashtags to video_metrics, filter last-month dates, SUM(views) by hashtag, order desc, and LIMIT 5. Introduces multi-table joins and aggregate ranking.

8. How many distinct users uploaded at least one video every day for the past week?

   Aggregate COUNT(DISTINCT upload_date) per user_id for the last seven days and HAVING count_date = 7. Demonstrates “all-days” logic via distinct counts.

9. Compute the average daily watch-time (minutes) per user for the current month.

   Sum watch_seconds / 60 per user per day, average those daily sums with a second-level aggregate. Tests nested grouping and unit conversion.

10. What percentage of yesterday’s new followers came from the “For You” feed vs. profile visits?

    Filter follows to yesterday, use CASE WHEN source = 'ForYou' THEN 1 END to count each source, divide by total follows, and round to two decimals. Validates conditional aggregation and ratio calculations.

Intermediate TikTok SQL Interview Questions (3–5 Years)

If you’re applying for mid-level data roles like TikTok Data Scientist or Data Engineer, your SQL screen will lean heavily on data science SQL interview questions involving cohort segmentation, performance tuning, and multi-layered metric design. At this level, you’ll also face questions that explore how efficiently you can solve problems—i.e., not just if your query works, but if it works well.

Interviewers may ask you to analyze creator retention, compare engagement across cohorts using window functions, or identify growth anomalies in a dataset. You’ll likely encounter EXPLAIN plans, nested CTEs, or subqueries that mimic what TikTok’s internal analytics pipelines look like. These problems don’t just assess your SQL—they reveal your approach to trade-offs between query readability, efficiency, and accuracy.

1. Do creators ship more orders to their primary address or to alternates? Return the percentage of “home” orders.

   Join transactions → users, flag rows where ship_addr_id = primary_addr_id, and compute 100 * SUM(home_flag) / COUNT(*) as home_address_percent. Because home addresses may be NULL or outdated, you’ll need a CASE WHEN guard and a sensible denominator. Interviewers probe your handling of NULLs, ratio math, and how you might index (user_id, ship_addr_id) to keep the join fast on billions of orders.

2. For 2020, show monthly active users, transaction counts, and GMV. How would you structure the query for maintainability?

   Derive month with DATE_TRUNC('month', order_ts), use a CTE to pre-aggregate order facts, then a final GROUP BY month that outputs three metrics. Follow-up discussion usually dives into why you chose CTEs over nested sub-queries and how a date-based partition or sort key accelerates the scan.

3. Within every department, list the top 3 salaries (or fewer if head-count is smaller) and include employee name, department, salary.

   Apply DENSE_RANK() OVER (PARTITION BY dept_id ORDER BY salary DESC) and filter rank ≤ 3. Sorting by dept_name ASC, salary DESC rounds things out. TikTok uses this to gauge fluency with window functions and edge-case handling (e.g., duplicate salaries, tiny teams).

4. Find all employees who joined TikTok before their manager did.

   Self-join employees e JOIN managers m ON e.manager_id = m.employee_id, filter where e.hire_date < m.hire_date, and project e.full_name. Beyond correctness, be ready to explain clustering on (manager_id, hire_date) so the inequality join remains efficient.

5. Compute the daily post-success rate for January 2020, where success = post_submitted ÷ post_entered.

   Aggregate the events table twice (submits vs. enters) or use conditional sums in a single pass, then divide and round. Clarify how you handle same-day duplicates—even though the prompt says “one post per day,” good mid-level answers still guard against dirty data.

6. Return the last transaction of each day (ID, timestamp, amount) and order chronologically. How would you keep it fast on a 3-TB table?

   Use ROW_NUMBER() OVER (PARTITION BY DATE(created_at) ORDER BY created_at DESC); filter on row_number = 1. Then discuss a descending sort key on created_at or a pre-aggregated daily snapshot that turns the analytic step into a simple lookup.

7. After every restock event, output the running total of sales for each product, ordered by product and date.

   First create a CTE that pairs each sale_date with the latest restock_date ≤ sale_date using a MAX-join. Then apply SUM(price) OVER (PARTITION BY product_id, restock_cycle ORDER BY sale_date) as sales_since_last_restock. The challenge lies in modelling “gaps-and-islands” correctly and explaining why a compound index on (product_id, sale_date) matters.

8. What percentage of users achieved at least one seven-day streak of visiting the same URL? Round to two decimals.

   Partition logs by user_id, url, subtract ROW_NUMBER() from visit_date to create streak groups, count length ≥ 7, de-duplicate users, then divide by total distinct users. Mid-level interviewers look for a clear gaps-and-islands explanation and thoughts on materializing daily visit flags to speed the heavy window scan.

9. Identify customers who placed more than three transactions in both 2019 and 2020. Which approach is most efficient at TikTok scale?

   Two common solutions: (a) year-specific CTEs with INTERSECT, or (b) a single GROUP BY customer_id, YEAR(order_date) and a HAVING SUM(year = 2019 AND txn_cnt > 3) > 0 AND SUM(year = 2020 AND txn_cnt > 3) > 0. Be prepared to defend your choice, reference columnar scan costs, and show how a (customer_id, order_date) sort key minimizes shuffle.

Advanced TikTok SQL Interview Questions (5+ Years)

Advanced SQL questions at TikTok are rare and usually reserved for Senior Data Scientists, Analytics Leads, or those stepping into product data strategy roles. These scenarios dive deep into business-critical logic, especially around algorithm health, monetization optimization, or safety checks within the creator economy.

For example, you may be asked to detect churn patterns in creators over rolling 90-day windows, evaluate fraudulent follower spikes, or build a real-time metric system for tracking engagement anomalies. These are not your average TikTok coding interview questions—they test your architectural thinking, ability to design efficient queries at scale, and domain-specific intuition.

Expect these prompts to include edge-case caveats and follow-up twists, forcing you to adapt your approach mid-solution. These challenges also blur into TikTok system design interview questions, so explaining your decisions clearly matters as much as the code itself.

1. How many “liker’s likers” does each user have?

   In a graph-style self-join, first gather the set of likers of a target user, then count distinct users who in turn have liked those likers. Interviewers expect a clear explanation of deduping, cyclic-edge prevention, and why an index on (liker_id, liked_id) avoids a quadratic scan on billions of edges.

2. Return the three-day rolling average of deposit amounts (positive values) for every calendar day, formatted YYYY-MM-DD.

   You’ll need a date-trunc staging CTE, conditional filtering (value > 0), and a window frame ROWS BETWEEN 2 PRECEDING AND CURRENT ROW. Top-tier answers discuss pre-aggregated daily snapshots that keep the rolling window from scanning raw events.

3. Detect creators who have lost ≥ 30 % of their average daily uploads in the most recent 90-day window compared with the previous 90 days. Return creator_id, prior_avg, current_avg, and the churn ratio.

   Partition uploads by creator, compute two non-overlapping 90-day windows with DATE_BUCKET logic, and subtract. Explain how you’d materialize 90-day creator aggregates in a fact table so the query is sub-second.

4. Identify accounts whose follower count grew more than 5 × the inter-quartile range of their historical daily gains within any 24-hour window last month.

   Create a per-account daily delta table, calculate IQR via PERCENTILE_CONT(0.25/0.75), then flag outliers with a windowed comparison. Interviewers probe your grasp of robust statistics and incremental percentile maintenance.

5. For every hour today, compute the watch-time Z-score of each viral sound versus its trailing seven-day hourly mean and standard deviation. Output rows where ABS(z) > 4.

   Requires a two-level aggregation: first roll up hourly watch-time, then join to a 7-day window table keyed by sound_id, hour_of_day. Discussion usually shifts to approximate stats tables and how you would stream new rows into a Kafka topic for live anomaly alerts.

6. A/B test: creators in variant B can pin comments. Calculate incremental revenue per creator, controlling for baseline CPM and heteroskedastic variance. Which SQL structure lets you surface creator-level lift and global p-value in one pass?

   Expect use of CTEs for pre-normalized revenue, a paired t-test via windowed aggregates, and a global summary. You’ll be grilled on why you chose paired vs. pooled variance in the SQL math.

7. Flag videos whose comment toxicity rate (toxic comments ÷ total comments) exceeds the 99th percentile within their category in the last 48 hours.

   Build a category-level percentile lookup table with APPROX_PERCENTILE, join to video-level toxicity rates, and filter. Advanced answers mention a late-arriving-comment backfill job so the metric doesn’t drift.

8. Compute the hourly ad-fill rate (filled_impressions ÷ auction_opportunities) for each region and raise an alert when the rate drops ≥ 15 % below the trailing-28-day regional mean.

   Two CTEs—one for today’s hour, one for the historical mean—joined on region. Interviewers will ask how you’d partition the ad events table on (region, event_hour) and push the heavy 28-day aggregation into a materialized view.

9. Daily “creator-share” fairness check: verify that the top 10 % of creators, by follower count, receive ≤ 40 % of total impressions in the For-You feed. Return the exact share and a boolean flag per day.

   Use a window function to rank creators by followers, bucket into deciles, then aggregate impressions. Discuss whether you’d pre-compute deciles in dimension tables to avoid daily resorting.

Understanding TikTok’s SQL interviews means recognizing how questions vary depending on the product stack. Below are the key platforms where SQL fluency shows up—and how each one differs in focus and complexity.

TikTok Ads (BytePlus)

In the TikTok Ads ecosystem, particularly BytePlus, SQL questions often revolve around attribution logic. A common example involves attributing ad clicks across a multi-touch funnel—requiring candidates to apply session windows to track user behavior over time. These problems test your ability to model sessions, handle timestamp sequences, and deduplicate user events across campaigns.

TikTok Live

SQL problems in TikTok Live revolve around real-time analytics. A typical challenge might involve computing gift revenue per stream, where data is ingested from Kafka streams into materialized views. You may need to reason about ingestion lags, stream freshness, or the best way to structure rolling aggregations on fast-moving data.

TikTok Shop

TikTok Shop leans heavily into e-commerce logic. A representative SQL interview question could involve calculating the SKU-level return rate, which involves filtering, counting, and joining on highly partitioned tables—usually with a shop_id as a key. You’ll be evaluated on both your ability to write efficient queries and how well you understand data partitioning trade-offs.

To stand out in the TikTok data engineer interview, you’ll need more than correct queries—you’ll need strategy. Here’s how to structure your approach for maximum impact.

Clarify Metric Definitions First

TikTok interviewers will often leave metric definitions intentionally vague. Whether it’s “active user,” “engagement,” or “return rate,” always clarify what the business context is and define your logic upfront. This shows product intuition in addition to SQL skill.

Think Out Loud—Highlight Trade-Offs

In both online assessments and live interviews, vocalizing your thought process is critical. Explain whether you’re choosing a temporary table or a CTE—and why. Interviewers at TikTok often evaluate clarity over cleverness, especially when you explain technical decisions.

Check Execution Plans (Even Theoretically)

While you won’t always get to run EXPLAIN or view query plans during the interview, referencing them in your explanation shows deeper understanding. For performance-focused questions, mention how indexes, table scans, or joins could affect performance.

Prioritize Readability Over Cleverness

Nested subqueries and over-optimized logic might work—but interviewers prefer readable, well-structured SQL that’s easy to debug and scale. A clear WITH clause or modular logic often beats a clever one-liner.

Want to dive deeper? Check out Advanced SQL Concepts or book a Mock TikTok SQL Interview to simulate real pressure.

How often is SQL tested in TikTok data interviews?

SQL is tested in almost every data-focused role at TikTok—from interns to senior engineers. You can expect SQL to make up a full round in most interviews, especially for Data Analyst and Data Engineer positions.

How often is SQL tested in TikTok data interviews?

SQL is tested in almost every data-focused role at TikTok—from interns to senior engineers. You can expect SQL to make up a full round in most interviews, especially for Data Analyst and Data Engineer positions.

TikTok’s SQL interview process is less about textbook syntax and more about storytelling with data. Candidates should master core clauses—especially joins, window functions, and date logic—while demonstrating a strong grasp of business context and metric accuracy. You’ll be tested on edge cases around creator engagement, content ranking, or real-time revenue. Remember, interviewers aren’t just looking for a working query—they’re evaluating how you frame problems, clarify assumptions, and trade off between clarity and performance.

To level up, try our AI Interviewer, schedule a Mock Interview, or explore a structured SQL Learning Path to practice exactly how TikTok hires.