Data Manipulation (SQL/Python) interview questions

Showing 301–330 of 446 results

• Write SQL to compute signup and retention liftMedium

  This prompt evaluates SQL proficiency in time-windowed cohort analysis, event deduplication, date arithmetic, and per-variant aggregation for A/B experiments, categorized under Data Manipulation (SQL/Python) and targeted at an intermediate-level Data Scientist role.

  OpenAIData ScientistData Manipulation (SQL/Python)Technical Screen
• Classify Tree Nodes in SQLMedium

  Tests the ability to write conditional logic and subqueries/joins in SQL to classify hierarchical tree nodes into 'Root', 'Inner', or 'Leaf'.

  OracleSoftware EngineerData Manipulation (SQL/Python)Technical Screen
• Parse logs and query by timeMedium

  This question evaluates competency in parsing and structuring log lines, interpreting timestamps and time zones, handling malformed input, designing insert/query APIs, reasoning about scalability and indexing, performing time/space complexity analysis, and planning basic tests.

  OracleSoftware EngineerData Manipulation (SQL/Python)Onsite
• Rank Departments by Student CountMedium

  Tests SQL proficiency in outer joins, aggregation with null handling, and deterministic multi-column sorting.

  OracleSoftware EngineerData Manipulation (SQL/Python)Onsite
• Build SQL pivot with lookups and currency conversionMedium

  This question evaluates proficiency in data manipulation using SQL or SQL-like Python transforms, covering point-in-time currency conversion, temporal joins for FX lookups, joins to enrich product/customer metadata, aggregation and pivot-style reporting, ranking, and data-quality checks for missing FX.

  OtherData ScientistData Manipulation (SQL/Python)Technical Screen
• Continue Entry Numbers for a Daily Incremental BatchMedium

  Continue per-context entry numbering for a daily batch using stored maximums, ordered row numbers, and correct new-context handling.

  OtherAnalytics EngineerData Manipulation (SQL/Python)Technical Screen
• Number Entries Within Each ConversationMedium

  Assign one-based entry numbers per conversation using timestamp and identifier ordering in a PostgreSQL window-function query.

  OtherAnalytics EngineerData Manipulation (SQL/Python)Technical Screen
• Solve window-function SQL without joinsMedium

  This question evaluates proficiency with SQL window functions, including running and cumulative sums, LAG/forward-fill event logic, ranking functions (RANK/DENSE_RANK/ROW_NUMBER), tie-breaking strategies, and temporal date arithmetic for payments and subscription events.

  OtherData ScientistData Manipulation (SQL/Python)Technical Screen
• Write SQL to analyze response accuracy and speedMedium

  This question evaluates proficiency with SQL-based data manipulation and analytical querying on relational assessment data, including aggregation, grouping, joins, windowed ranking, and use of PostgreSQL statistical functions to analyze accuracy and response-time metrics.

  OtherData ScientistData Manipulation (SQL/Python)Online Assessment
• Compute per-coin quarterly amounts and totalsEasy

  This question evaluates competence in SQL data manipulation—specifically time-based conditional aggregation, grouping, joins, and handling entities with no activity—to compute per-coin quarterly sums and year-to-date totals.

  OthersData ScientistData Manipulation (SQL/Python)Online Assessment
• Find Every Project with the Most EmployeesMedium

  Build a PostgreSQL aggregation that returns every project tied for the largest employee count. This compact exercise checks grouping, maximum selection, tie preservation, empty-input behavior, and careful use of primary-key guarantees.

  Otter.AiData EngineerData Manipulation (SQL/Python)Technical Screen
• Find the Highest-Paid Employee in Each DepartmentMedium

  Work through a tie-aware PostgreSQL problem that finds every highest-paid employee in each department. It assesses relational reasoning, correct joins, duplicate-name handling, and precise output semantics without assuming a single winner.

  Otter.AiData EngineerData Manipulation (SQL/Python)Technical Screen
• Clean and Summarize User Purchase Data EfficientlyMedium

  This question evaluates proficiency in data cleaning, aggregation, and time-series manipulation within the Data Manipulation (SQL/Python) domain, focusing on filtering by group counts and calculating inter-event timing metrics.

  PayPalData ScientistData Manipulation (SQL/Python)Onsite
• Compare WHERE vs HAVING with aggregatesMedium

  Compare WHERE vs HAVING with aggregates 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.

  PayPalData ScientistData Manipulation (SQL/Python)Onsite
• Explain Window Functions and Joins in SQL and PythonMedium

  This question evaluates proficiency in SQL and Python data manipulation, covering window functions, join semantics (INNER, LEFT, RIGHT, FULL, CROSS), ranking functions, group and string aggregation, type casting, and building row-wise accumulations in Python, and it falls under the Data Manipulation (SQL/Python) category.

  PayPalData ScientistData Manipulation (SQL/Python)Onsite
• Identify Session with Maximum Overlapping Sessions CountMedium

  This question evaluates temporal interval reasoning, set-based querying and aggregation skills, focusing on identifying and counting overlapping time intervals using SQL or equivalent Python data-manipulation tools.

  PayPalData ScientistData Manipulation (SQL/Python)Onsite
• Identify Users with Specific Page Visit SequenceMedium

  This question evaluates event-sequence detection and temporal ordering skills in SQL, focusing on concepts such as windowing, adjacent-row reasoning, and session-scoped grouping to identify ordered page visits.

  PayPalData ScientistData Manipulation (SQL/Python)Onsite
• Write SQL to flag Venmo ATOMedium

  This question evaluates a candidate's ability to construct complex SQL to detect account-takeover (ATO) signals in a payments platform, assessing skills in event-time windowing, deduplication, joins across user/login/transaction tables, and categorical flagging of new devices or IPs.

  PayPalData ScientistData Manipulation (SQL/Python)Technical Screen
• Write SQL using HAVING and window functionsEasy

  This question tests the ability to filter aggregated data using the HAVING clause and compute relative metrics using window functions to identify high-frequency transaction patterns.

  PayPalData ScientistData Manipulation (SQL/Python)Technical Screen
• Write conditional aggregates with CASE WHENMedium

  Write conditional aggregates with CASE WHEN 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.

  PayPalData ScientistData Manipulation (SQL/Python)Onsite
• Write conditional aggregation SQL queriesMedium

  This question evaluates proficiency with conditional aggregation in SQL, including the use of SUM(CASE WHEN ...) versus filtering with WHERE and the implications of placing aggregation filters in HAVING for grouped queries, within the Data Manipulation (SQL/Python) domain.

  PayPalData ScientistData Manipulation (SQL/Python)Onsite
• Analyze Global Engagement and Impressions with SQL QueriesMedium

  This question evaluates proficiency in SQL-based data manipulation and analytical querying, covering aggregation, grouping, ranking, and the correlation of impressions with user engagement and feature usage metrics.

  PinterestData ScientistData Manipulation (SQL/Python)Onsite
• Calculate the Percentage of Highly Active Users by CountryMedium

  Calculate highly active users by country over seven calendar days using distinct active dates and same-day surface diversity, with percentage output.

  PinterestData ScientistData Manipulation (SQL/Python)Technical Screen
• Clean and Aggregate Transactions for Finance DashboardMedium

  This question evaluates data cleaning and transformation competencies including pandas-based vectorized operations, categorical mapping and feature flagging, as well as aggregation and summarization of transactional data in the data manipulation (SQL/Python) domain.

  PinterestData ScientistData Manipulation (SQL/Python)Onsite
• Compute CTR by format for new US usersMedium

  This question evaluates a candidate's competency in SQL data manipulation and aggregation, covering joins, date-based filtering for user cohorts, deduplication, conditional summing, null-safe arithmetic, rounding, and ordering to compute a business metric (CTR).

  PinterestData ScientistData Manipulation (SQL/Python)Technical Screen
• Compute percent of first-cancel users who never returnEasy

  This question evaluates a candidate's competency in data manipulation and temporal analysis using SQL/Python, focusing on computing user-level metrics and correctly ordering time-based events.

  PinterestData AnalystData Manipulation (SQL/Python)Technical Screen
• Compute percent of first-cancelled users who never rebookMedium

  This question evaluates proficiency in SQL data manipulation—specifically use of window functions, CTEs, deduplication by earliest timestamp, temporal comparisons, and cohort-level aggregation—within the Data Manipulation (SQL/Python) domain for a Data Analyst role in health‑tech product analytics.

  PinterestData AnalystData Manipulation (SQL/Python)Technical Screen
• Find the Share of Power Listeners per Region from Play EventsMedium

  Write a PostgreSQL query that, for a trailing 7-day window, classifies listeners per region as active or 'power' users based on how many distinct days they listened and whether they used at least three different playback devices on any single one of those days, then reports the power-user share per region.

  PinterestData ScientistData Manipulation (SQL/Python)Technical Screen
• Find top video category by average timeMedium

  This question evaluates a candidate's ability to perform data manipulation and aggregation in Python (pandas), covering skills such as filtering, handling nulls and nonpositive values, mapping categorical IDs to names, rounding numerical results, and applying tie-breaking rules.

  PinterestData ScientistData Manipulation (SQL/Python)Technical Screen
• Implement Binary Search for Policy Violation LogsMedium

  This question evaluates understanding of algorithmic search and data manipulation skills, specifically applying binary search over date-ordered logs and composing efficient queries using SQL and Python.

  PinterestData ScientistData Manipulation (SQL/Python)Onsite