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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

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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)

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

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

Showing 181–210 of 446 results

  • Parse CSV and format transactionsMedium

    This question evaluates a candidate's ability to parse and format structured transactional data, emphasizing CSV parsing, trimming whitespace, numeric formatting to two decimal places, and preserving input row order.

    KikoffSoftware EngineerData Manipulation (SQL/Python)Technical Screen
  • Count IoT devices matching date and thresholdMedium

    This question evaluates handling of paginated REST APIs, JSON parsing and nested-data filtering, month/year date parsing, and numeric threshold comparisons, reflecting skills in data retrieval and programmatic data manipulation.

    KneronSoftware EngineerData Manipulation (SQL/Python)Online Assessment
  • Query Active Loans by Borrower ID in SQLMedium

    This question evaluates proficiency with SQL data aggregation, grouping, and ordering to compute borrower-level loan totals, assessing practical skills in relational data manipulation within the Data Manipulation (SQL/Python) domain.

    LendingClubData ScientistData Manipulation (SQL/Python)Technical Screen
  • Analyze member video posting behavior by countryEasy

    A LinkedIn data scientist technical-screen question on member video-posting behavior using two relational tables. It tests joins, first-event derivation with MIN/GROUP BY, conditional grouping for US vs non-US comparison, and distinct-member counts per country with a length filter.

    LinkedInData ScientistData Manipulation (SQL/Python)Technical Screen
  • Compute each member’s current notification statusEasy

    A LinkedIn data scientist SQL screen: given an end-of-January notification-status snapshot and a February log of turn_on/turn_off actions, compute each member's current status as of 2020-02-29, keeping members from either table. Follow-ups explore reconstructing state when the action log has no date column — unsolvable in general, but recoverable via action-count parity when every action is a guaranteed valid transition.

    LinkedInData ScientistData Manipulation (SQL/Python)Technical Screen
  • Count Article Types ViewedMedium

    Count Article Types Viewed evaluates SQL or pandas logic, joins, grouping, window functions, null handling, edge cases, and validation in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

    LinkedInData ScientistData Manipulation (SQL/Python)Technical Screen
  • Device Status without TimestampsMedium

    This question evaluates data manipulation and behavioral analytics competencies, emphasizing aggregation, deduplication, distinct counts, and date-based filtering to measure author and viewer activity.

    LinkedInData ScientistData Manipulation (SQL/Python)Technical Screen
  • [SQL] Job Ad Metrics with Applicant FilterMedium

    This question evaluates proficiency in SQL data manipulation and deduplication, focusing on aggregations, distinct counts, and conditional filtering of event records (views vs applies).

    LinkedInData ScientistData Manipulation (SQL/Python)Technical Screen
  • Query and transform marketplace data in SQL/PythonMedium

    This question evaluates mastery of data manipulation and analytics skills in the Data Manipulation (SQL/Python) domain, including SQL aggregation and window functions, time-based joins and grouping, deduplication of status streams, handling late-arriving events, and implementing rolling metrics using both SQL and pandas.

    LyftData ScientistData Manipulation (SQL/Python)Onsite
  • Find users with multi-country successful loginsMedium

    This question evaluates proficiency in relational data manipulation, text normalization, aggregation, deduplication, and sorting within the Data Manipulation (SQL/Python) domain, focusing on practical query-writing and data-processing skills.

    Marshall WaceSoftware EngineerData Manipulation (SQL/Python)Onsite
  • Calculate cost from orders with SQLMedium

    This question evaluates a candidate's ability to perform SQL data manipulation and business-rule calculations, including conditional joins, date-range filtering, and numeric arithmetic for discounts and taxes.

    MathWorksSoftware EngineerData Manipulation (SQL/Python)Online Assessment
  • Analyze Bookstore Data to Identify Top Payment MethodsMedium

    This question evaluates data manipulation and analytical SQL/Python competency, focusing on relational joins, aggregations, string filtering, ranking, and proportion calculations across multiple tables in a bookstore schema.

    MetaData ScientistData Manipulation (SQL/Python)Technical Screen
  • Analyze Hashtag Follow Behavior with SQL QueriesMedium

    This question evaluates a candidate's competency in data manipulation and analytical SQL techniques—specifically aggregation, filtering, joins, and calculation of proportions—to analyze hashtag follow behavior.

    MetaData ScientistData Manipulation (SQL/Python)Onsite
  • Analyze Mobile Promo Orders with SQL Query and MetricsMedium

    This question evaluates SQL data-manipulation and analytical competencies, including filtered joins, conditional aggregation, date-window filtering, and business-metric design to measure promotional performance on mobile orders.

    MetaData ScientistData Manipulation (SQL/Python)Technical Screen
  • Analyze Oculus App Engagement with SQL QueriesMedium

    This question evaluates proficiency in SQL-based data manipulation and aggregation, time-window filtering, percentage calculations, and the application of basic statistical hypothesis testing for comparing user engagement across categories.

    MetaData ScientistData Manipulation (SQL/Python)Onsite
  • Analyze Seller Activity and Vehicle Listing InteractionsMedium

    This interview question evaluates SQL or pandas logic, joins, grouping, window functions, null handling, edge cases, and validation in a realistic interview setting. A strong answer for Analyze Seller Activity and Vehicle Listing Interactions states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

    MetaData ScientistData Manipulation (SQL/Python)Technical Screen
  • Analyze Social Media Engagement with SQL QueriesMedium

    This question evaluates SQL data-manipulation and analytics competencies, including aggregation, DISTINCT counting, joins between view and action logs, date-range filtering, and conditional metric computation from event data.

    MetaData ScientistData Manipulation (SQL/Python)Technical Screen
  • Analyze Thirty-Day Ad Performance with SQLMedium

    This problem tests proficiency in window functions, time-based aggregation, and join logic to analyze multi-dimensional advertising performance metrics.

    MetaData ScientistData Manipulation (SQL/Python)Onsite
  • Analyze Top Call Initiators and Active French Video CallersMedium

    This question evaluates proficiency in SQL-based data manipulation and analytics—specifically aggregation, grouping, time-window filtering, and percentage calculations—by asking for the top call initiators over the last seven days and the proportion of active users in France on video calls yesterday, and is categorized under Data Manipulation (SQL/Python) for a Data Scientist role with a focus on practical application. It is commonly asked in technical interviews to assess a data scientist's ability to extract actionable engagement metrics from event logs, reason about temporal windows and population denominators, and produce accurate, explainable reporting from production-style datasets.

    MetaData ScientistData Manipulation (SQL/Python)Technical Screen
  • Analyze User Engagement and Spammer Read-Rate in SQLMedium

    This question evaluates SQL-based data manipulation and analytics skills—specifically joins, time-window filtering, aggregation, and metric calculation for measuring user engagement and spammer read-rate—within the Data Manipulation (SQL/Python) domain.

    MetaData ScientistData Manipulation (SQL/Python)Technical Screen
  • Calculate Ad Performance with Click-Through and Conversion RatesMedium

    This question evaluates a candidate's competency in designing and computing ad-performance KPIs by performing data manipulation, joining event tables, applying time-window filters, and aggregating metrics using SQL or Python.

    MetaData ScientistData Manipulation (SQL/Python)Onsite
  • Calculate CTR and ad revenueMedium

    This problem tests your ability to clean and join datasets, handle currency conversion, and aggregate metrics at different granularities.

    MetaData ScientistData Manipulation (SQL/Python)Technical Screen
  • Calculate Daily Survey Response Rates by CountryMedium

    Write a PostgreSQL query to compute daily survey response rates at the country level using distinct user counts while handling orphan response logs and edge cases.

    MetaData ScientistData Manipulation (SQL/Python)Technical Screen
  • Calculate French User Engagement and U.S. Call DurationMedium

    This question evaluates proficiency in SQL-based data manipulation and analytics, testing skills such as joining event and profile tables, aggregating metrics, and computing user-level engagement proportions and average durations.

    MetaData ScientistData Manipulation (SQL/Python)Technical Screen
  • Calculate Survey Response Rate and Quality Metric in SQLMedium

    This question evaluates SQL data-manipulation competencies such as aggregation, NULL handling, deduplication (first-per-group), and computing engagement and quality metrics from event logs.

    MetaData ScientistData Manipulation (SQL/Python)Technical Screen
  • Calculate Total Revenue in USD Using SQL QueryMedium

    This question evaluates proficiency in SQL data manipulation, focusing on joins, aggregations, and handling currency conversion across relational tables.

    MetaData ScientistData Manipulation (SQL/Python)Onsite
  • Calculate Video Call Usage Metrics by Country and DateMedium

    This question evaluates data manipulation and product-analytics skills, focusing on joining user and call datasets, aggregations for distinct-user percentages, and per-DAU duration calculations.

    MetaData ScientistData Manipulation (SQL/Python)Technical Screen
  • Calculate and Compare Survey Response Rates for User TenureMedium

    This question evaluates proficiency in relational data manipulation and metric computation, including SQL joins, aggregation and cohort-based comparisons relevant to survey response analysis, and is categorized under Data Manipulation (SQL/Python) for a Data Scientist role.

    MetaData ScientistData Manipulation (SQL/Python)Onsite
  • Compare Survey Satisfaction for New and Established UsersMedium

    Tests your ability to write PostgreSQL queries that segment users into cohorts based on date logic, filter invalid survey events, and calculate aggregate satisfaction metrics.

    MetaData ScientistData Manipulation (SQL/Python)Technical Screen
  • Compute 7-day views and reactions by relationshipMedium

    This question evaluates skills in SQL-based data manipulation—specifically deduplication by longest duration, join-based attribution of reactions to views, window functions, distinct counts, aggregations, and date-range filtering within the Data Manipulation (SQL/Python) domain.

    MetaData ScientistData Manipulation (SQL/Python)Technical Screen

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