Topic

Data Manipulation (SQL/Python) interview questions

446 Data Manipulation (SQL/Python) questions from 106 companies, with hints, runnable code and solution walkthroughs.

Questions446

42 easy394 medium10 hard

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Data Manipulation (SQL/Python) interview questions by the numbers

Last updated October 28, 2026

Katkat has 446 Data Manipulation (SQL/Python) interview questions from 106 companies; the most come from Meta (17%), Amazon (9%) and TikTok (5%). By difficulty: 9% easy, 88% medium and 2% hard. The most common round is Technical Screen (57%). Frequent concepts include SQL, Aggregation, Window Functions and Data Manipulation.

Companies (106)

Meta17% · 74
Amazon9% · 38
TikTok5% · 23
Capital One4% · 18
DoorDash4% · 16
Pinterest3% · 15
OpenAI2% · 11
Airbnb2% · 10

Difficulty

Easy9% · 42
Medium88% · 394
Hard2% · 10

Interview rounds

Technical Screen57% · 256
Onsite30% · 134
Online Assessment9% · 39
HR Screen4% · 16
Other0% · 1

Roles

Data Scientist76% · 339
Software Engineer11% · 47
Data Engineer5% · 23
Machine Learning Engineer2% · 11
Business Intelligence Engineer2% · 10

Frequent concepts

SQL8% · 35
Aggregation4% · 20
Window Functions4% · 16
Data Manipulation3% · 15
sql3% · 15
Joins2% · 11

Most mentioned in interview experiences

1.Compute active ad revenue by creation source3 reports
2.Compute dasher pay from deliveries3 reports
3.Write complex SQL on DoorDash data3 reports
4.Analyze Streaming Data with SQL for Monthly Trends2 reports
5.Calculate the Percentage of Highly Active Users by Country2 reports
6.Compute courier pay with peak-hour rules2 reports
7.Compute SHOP spend share and model performance2 reports
8.Convert Dates to Calendar Quarter Labels in SQL/Python2 reports
9.Define and compute shop visibility in SQL2 reports
10.Design payment-to-invoice matcher with priorities2 reports

Counted from 111 candidate interview experiences on Katkat. How we collect and count

Showing 211–240 of 446 results

Compute CTR overall and by campaign typeMedium
This question evaluates data manipulation and analytics skills, specifically SQL aggregations, deduplication of event logs, time-window filtering, joins to campaign metadata, and safe ratio computation including division-by-zero handling.
MetaData ScientistData Manipulation (SQL/Python)Technical Screen
Compute Each Advertiser's Share of Shop Ad SpendMedium
This problem tests your ability to perform windowed aggregations, conditional filtering, and percentage calculations across multiple tables using SQL.
MetaData ScientistData Manipulation (SQL/Python)Onsite
Compute High-Call Usage RatesHard
This question evaluates a candidate's ability to perform advanced data manipulation and analytical SQL tasks, including joins, aggregations, timezone-aware date handling, rolling window metrics, distinct user counting, and counting user participations across roles.
MetaData ScientistData Manipulation (SQL/Python)Technical Screen
Compute SHOP spend share and model performanceMedium
This question evaluates proficiency in SQL/Python data manipulation and analytics, focusing on aggregations, joins, time-window computations, handling missing or zero values, and deriving key metrics such as revenue share and revenue-per-conversion.
MetaData ScientistData Manipulation (SQL/Python)Technical Screen
Compute Shop Visibility Rate Using SQL and PythonMedium
This question evaluates proficiency in data manipulation and metric design using SQL and Python, focusing on event-level aggregation, user deduplication, and definition of a business visibility metric within the Data Manipulation (SQL/Python) domain.
MetaData ScientistData Manipulation (SQL/Python)Onsite
Compute active ad revenue by creation sourceEasy
This question evaluates proficiency in data manipulation and analytics, testing skills such as joining event and impression tables, aggregating time-series revenue, and counting distinct active ads broken down by creation source.
MetaData ScientistData Manipulation (SQL/Python)Onsite
Compute ad impression conversion ratesMedium
This question evaluates proficiency in time-based event attribution, aggregation of business metrics (impressions, conversion rates, spend and revenue), timestamp handling, and user/ad-level matching within the Data Manipulation (SQL/Python) domain, emphasizing practical application over abstract theory.
MetaData ScientistData Manipulation (SQL/Python)Onsite
Compute ad revenue metrics by geography in SQLMedium
In the Data Manipulation (SQL/Python) domain, this intermediate-level question evaluates SQL aggregation, join logic between impressions and clicks, date-based UTC attribution, user-geo joins, and null/divide-by-zero-safe computation of derived metrics (CTR, RPM) at the country-day granularity.
MetaData ScientistData Manipulation (SQL/Python)Technical Screen
Compute cohort GMV and payer rate with edge casesMedium
This question evaluates a data scientist's competency in cohort-based GMV calculation, payer-rate measurement, and handling common edge cases across events, orders, payments, and refunds.
MetaData ScientistData Manipulation (SQL/Python)Technical Screen
Compute daily active adsEasy
This question tests your ability to aggregate time-series event data and perform distinct counts based on state transitions.
MetaAnalytics EngineerData Manipulation (SQL/Python)Onsite
Compute multi-account user distribution and unread pctEasy
This question evaluates a data scientist's ability to perform relational data manipulation and aggregation with SQL or Python, covering competencies such as handling one-to-many joins, grouping and bucketing counts, deduplicating across related tables, and computing cohort-level percentages from boolean flags.
MetaData ScientistData Manipulation (SQL/Python)Technical Screen
Compute reservation diff for largest memberMedium
This question evaluates competency in relational data manipulation and aggregation using SQL or Python, including joins, grouping, and handling missing referrer relationships.
MetaData EngineerData Manipulation (SQL/Python)Technical Screen
Compute shop visibility and intent metrics in SQLMedium
This question evaluates SQL-based data manipulation and analytical competencies in the Data Manipulation (SQL/Python) domain, focusing on deduplication, time-zone-aware timestamp handling, window functions, aggregation, and construction of visibility, CTR, and intent metrics for shops.
MetaData ScientistData Manipulation (SQL/Python)Technical Screen
Compute this-year spend share of last-year whalesEasy
This question tests the ability to perform cohort analysis and time-based filtering to calculate the contribution of high-value segments across different fiscal years.
MetaData ScientistData Manipulation (SQL/Python)Onsite
Compute time-spent percentage by app categoryMedium
This question evaluates proficiency in data manipulation and analytics, specifically SQL/Python skills for joining tables, aggregating session durations, handling missing app-category mappings, and computing percentage time spent over a recent seven-day window.
MetaData ScientistData Manipulation (SQL/Python)Onsite
Compute unconnected 60s posts and reactions averagesMedium
This question evaluates data manipulation skills in SQL and Python, focusing on deduplication, join logic between event and reaction tables, date handling for string-typed dates, and aggregation to compute per-post metrics.
MetaData ScientistData Manipulation (SQL/Python)Technical Screen
Compute unread and multi-account user percentagesMedium
This question evaluates understanding of SQL data manipulation skills such as aggregations, joins, NULL handling, deduplication reasoning, and user- versus person-level computations for accurate percentage metrics.
MetaData ScientistData Manipulation (SQL/Python)Technical Screen
Compute view prevalence from views and labelsMedium
This question evaluates a candidate's competency in temporal joins, stateful deduplication of moderation decisions, time-aware aggregation, and prevalence metric calculations within content-moderation analytics.
MetaData ScientistData Manipulation (SQL/Python)Technical Screen
Count heavy callers in 7 daysMedium
This question tests the ability to handle temporal windowing and aggregation to identify user behavior patterns based on call activity frequency.
MetaData ScientistData Manipulation (SQL/Python)Technical Screen
Define and compute shop visibility in SQLMedium
This question evaluates the ability to define and compute shop-level visibility KPIs in SQL, testing skills in metric design (visibility_rate and impression_share), deduplication, session/window filtering, and data-quality concerns such as late-arriving events and bot filtering.
MetaData ScientistData Manipulation (SQL/Python)Onsite
Design SQL Query for Shop Visibility and User Activity MetricsMedium
This question evaluates proficiency in SQL-based data manipulation—aggregation, JOINs, CASE expressions, window functions, ranking, and metric design for user tenure segmentation and temporal activity comparisons.
MetaData ScientistData Manipulation (SQL/Python)Technical Screen
Determine Old vs. New Users' Shop Visibility ChangesMedium
This question evaluates SQL-focused data manipulation skills and analytical competency in cohort definition and metric selection for comparing behavioral propensities, specifically assessing how to measure the likelihood of old versus new users switching a shop profile to invisible.
MetaData ScientistData Manipulation (SQL/Python)Onsite
Determine Unhealthy Oculus Usage with SQL AnalysisMedium
This question evaluates SQL-based data manipulation and time-series analysis competencies, including sessionization, user-day aggregation, and detection of sustained behavioral patterns in event data; it is categorized under Data Manipulation (SQL/Python) and targets practical application skills.
MetaData ScientistData Manipulation (SQL/Python)Onsite
Find A Low-Quality Annotator From Label DataHard
This problem tests your ability to clean noisy labeling datasets and design metrics to detect annotator bias or performance degradation.
MetaMachine Learning EngineerData Manipulation (SQL/Python)Onsite
Find least active countriesMedium
This question tests the ability to perform multi-step data aggregation using SQL, specifically focusing on joining, filtering by date ranges, calculating distinct counts, and identifying bottom-tier performers across different time periods.
MetaData ScientistData Manipulation (SQL/Python)Onsite
Find the most-used appMedium
This question evaluates data manipulation and metrics-definition skills, emphasizing SQL and Python proficiency for aggregating user activity and joining app metadata within analytics workflows.
MetaData ScientistData Manipulation (SQL/Python)Onsite
Identify 3-Person Call Cycles in Video-Calling AppMedium
This question evaluates SQL data-manipulation competency, including aggregation, distinct counting, date filtering and relational pattern detection (three-node cycles) to measure user engagement and group-call signals.
MetaData ScientistData Manipulation (SQL/Python)Technical Screen
Identify Top 10 Users by Average Call DurationMedium
This question evaluates SQL-based data manipulation and time-series aggregation skills, specifically computing per-user metrics from timestamped event data such as call durations.
MetaData ScientistData Manipulation (SQL/Python)Onsite
Rank Ads by Conversion Rate for Top 10 PerformersMedium
This question evaluates data manipulation and analytics skills for computing time-windowed ad performance metrics, including aggregations of impressions and revenue, join logic between impression and conversion records, conversion-rate calculation, and ranking.
MetaData ScientistData Manipulation (SQL/Python)Onsite
Recommend two-hop follows in PythonMedium
This question evaluates fluency with graph traversal, set operations, deduplication, and frequency-based ranking in Python, targeting manipulation of directed "follows" relationships represented as dictionaries and is categorized under Data Manipulation (SQL/Python).
MetaData EngineerData Manipulation (SQL/Python)Onsite