Data Manipulation (SQL/Python) Interview Questions

Showing 151–180 of 256 results

• MolocoMedium·Data Scientist
  Find top advertisers and cost per KPI

  This question evaluates proficiency in data manipulation and analytical SQL/Python skills, including aggregation, ranking with ties, dataset joins at a defined grain, date-based filtering relative to campaign launch dates, and handling division-by-zero scenarios when computing cost metrics.

  Data Manipulation (SQL/Python)Technical Screen
• NVIDIAMedium·Software Engineer
  Implement 2D convolution using NumPy slicing

  This question evaluates a candidate's proficiency with array manipulation and vectorized numerical computation using NumPy, focusing on 2D convolution concepts such as slicing, broadcasting, and filter application.

  Data Manipulation (SQL/Python)Technical Screen
• NetflixMedium·Machine Learning Engineer
  Transform flat keys into nested dictionary

  This question evaluates proficiency in parsing and restructuring hierarchical parameter keys into nested dictionary structures, string manipulation for grouping, handling nonconforming keys and duplicates, and reasoning about time and space complexity.

  Data Manipulation (SQL/Python)Technical Screen
• NotionMedium·Data Scientist
  Analyze Document Collaboration CSVs

  This problem tests your ability to clean, join, and analyze messy user-activity data using pandas or SQL, focusing on window functions and handling missing values.

  Data Manipulation (SQL/Python)Technical ScreenSQLPandasData Cleaning
• NotionEasy·Data Scientist
  Analyze document collaboration from CSV logs

  This question evaluates data manipulation and analytical competencies—particularly deduplication, joins, aggregation, co-occurrence counting, and metric definition—applied to CSV access logs and user metadata.

  Data Manipulation (SQL/Python)Technical Screen
• NotionMedium·Data Scientist
  Analyze document collaboration patterns

  This question evaluates a candidate's competency in data manipulation and analytics—specifically joins, deduplication of user-document pairs, aggregation, and computation of user- and country-level collaboration metrics using SQL or Python.

  Data Manipulation (SQL/Python)Technical Screen
• NuroEasy·Data Engineer
  Compute unique duration by merging intervals

  This question evaluates a data engineer's proficiency in temporal data manipulation and interval-set reasoning using SQL and Python, specifically skills in merging overlapping intervals and computing deduplicated cumulative durations.

  Data Manipulation (SQL/Python)Technical Screen
• OpenAIHard·Data Scientist
  Compute churn with re-subscriptions

  This problem tests your ability to reconstruct user subscription states over time from raw event logs, specifically identifying active status to calculate monthly churn rates.

  Data Manipulation (SQL/Python)Technical ScreenSQLData ManipulationTime-series
• OpenAIEasy·Data Scientist
  Compute signup rate and retention from raw logs

  This question evaluates the ability to manipulate event-level logs to compute cohort metrics—specifically signup rate and D30 retention—testing proficiency in SQL/Python, event-time windowing, joins, aggregation, and understanding of upstream logging semantics.

  Data Manipulation (SQL/Python)Technical Screen
• OpenAIMedium·Data Scientist
  Debug and harden trial-assignment Python code

  This question evaluates understanding of experiment assignment, data integrity, reliability engineering, concurrency, observability, and privacy in data systems, testing competencies around randomness, idempotency, time semantics, retries, and failure-mode identification.

  Data Manipulation (SQL/Python)Technical Screen
• OpenAIHard·Data Scientist
  Handle repeated churn in SQL

  This problem tests your ability to handle complex temporal data by identifying user churn and re-subscription events, then calculating metrics like tenure and churn count per user.

  Data Manipulation (SQL/Python)Technical ScreenSQLWindow FunctionsTime Series
• OpenAIMedium·Software Engineer
  Parse and build binary data in Python

  This question evaluates proficiency in binary data handling and serialization/deserialization in Python, including little-endian integer encoding, length-prefixed payloads, robust error handling for short reads and invalid lengths, and testability and performance considerations.

  Data Manipulation (SQL/Python)Technical Screen
• OpenAIMedium·Machine Learning Engineer
  Train and analyze a classifier

  This question evaluates proficiency in end-to-end machine learning engineering, including exploratory data analysis, time-aware train/validation/test splitting, model baseline and improvement, class imbalance strategies, leakage-safe hyperparameter tuning, metric computation and calibration, error analysis, reproducible training pipelines with CLI/config/seed control, explainability (feature importance/SHAP and ablations), and documentation of risks, fairness checks and monitoring hooks. It is commonly asked to assess practical ability to manage the full ML lifecycle and data hygiene in production-like scenarios, testing applied Data Manipulation (SQL/Python) and Machine Learning competencies with an emphasis on practical application while also requiring conceptual understanding of evaluation, calibration and fairness.

  Data Manipulation (SQL/Python)Technical Screen
• OpenAIHard·Data Scientist
  Write SQL for repeat churn

  This question evaluates proficiency in event-time analytics and cohort churn metric computation using SQL, focusing on correct identification of first qualifying events and aggregation by experimental variant.

  Data Manipulation (SQL/Python)Technical Screen
• OpenAIMedium·Data Scientist
  Write SQL to compute signup and retention lift

  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.

  Data Manipulation (SQL/Python)Technical Screen
• OracleMedium·Software Engineer
  Classify Tree Nodes in SQL

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

  Data Manipulation (SQL/Python)Technical ScreenSQLPostgreSQLTrees
• OOtherMedium·Data Scientist
  Build SQL pivot with lookups and currency conversion

  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.

  Data Manipulation (SQL/Python)Technical Screen
• OOtherMedium·Analytics Engineer
  Continue Entry Numbers for a Daily Incremental Batch

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

  Data Manipulation (SQL/Python)Technical Screen
• OOtherMedium·Analytics Engineer
  Number Entries Within Each Conversation

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

  Data Manipulation (SQL/Python)Technical Screen
• OOtherMedium·Data Scientist
  Solve window-function SQL without joins

  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.

  Data Manipulation (SQL/Python)Technical Screen
• Otter.AiMedium·Data Engineer
  Find Every Project with the Most Employees

  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.

  Data Manipulation (SQL/Python)Technical Screen
• Otter.AiMedium·Data Engineer
  Find the Highest-Paid Employee in Each Department

  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.

  Data Manipulation (SQL/Python)Technical Screen
• PayPalMedium·Data Scientist
  Write SQL to flag Venmo ATO

  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.

  Data Manipulation (SQL/Python)Technical Screen
• PayPalEasy·Data Scientist
  Write SQL using HAVING and window functions

  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.

  Data Manipulation (SQL/Python)Technical ScreenSQLWindow FunctionsAggregations
• PinterestMedium·Data Scientist
  Calculate the Percentage of Highly Active Users by Country

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

  Data Manipulation (SQL/Python)Technical Screen
• PinterestMedium·Data Scientist
  Compute CTR by format for new US users

  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).

  Data Manipulation (SQL/Python)Technical Screen
• PinterestEasy·Data Analyst
  Compute percent of first-cancel users who never return

  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.

  Data Manipulation (SQL/Python)Technical Screen
• PinterestMedium·Data Analyst
  Compute percent of first-cancelled users who never rebook

  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.

  Data Manipulation (SQL/Python)Technical Screen
• PinterestMedium·Data Scientist
  Find the Share of Power Listeners per Region from Play Events

  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.

  Data Manipulation (SQL/Python)Technical Screensqlwindow-functionsaggregation
• PinterestMedium·Data Scientist
  Find top video category by average time

  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.

  Data Manipulation (SQL/Python)Technical Screen