Data Manipulation (SQL/Python) Interview Questions

Showing 181–210 of 256 results

• PinterestHard·Data Scientist
  Implement and Evaluate Pin Similarity in Python

  Practice a DS-focused Python similarity question using Pin representations, cosine similarity, top-k ranking, and explicit edge-case handling. Extend the implementation discussion to vectorized scaling, approximate retrieval, offline ranking metrics, leakage checks, diversity, and online evaluation.

  Data Manipulation (SQL/Python)Technical Screen
• PinterestMedium·Data Scientist
  Write Queries for Pinterest Engagement Tasks

  This question evaluates SQL-based data manipulation and Python set/co-occurrence computation skills, covering aggregation, joins, timestamp-based cohorting and windowing, freshness detection, CTR calculation, and similarity scoring within a data scientist context.

  Data Manipulation (SQL/Python)Technical Screen
• PRPlymouth Rock Assurance CorporationMedium·Data Scientist
  Calculate Ultimate Loss by Policy Term

  This question evaluates a candidate's ability to perform sequence-aware cumulative aggregation and partitioned data manipulation, assessing competency in calculating running totals (ultimate loss) across policy-term records.

  Data Manipulation (SQL/Python)Technical Screen
• Point72Medium·Data Scientist
  Convert Dates to Calendar Quarter Labels in SQL/Python

  This question evaluates a candidate's ability to perform date/time data transformation and normalization in SQL or Python, focusing on converting raw numeric dates into standardized quarter labels for aggregation and reporting.

  Data Manipulation (SQL/Python)Technical Screen
• Point72Medium·Data Scientist
  Write SQL for top student per department

  This question evaluates proficiency in SQL query formulation, including group-wise selection, joins, NULL handling, deterministic tie-breaking and ordering to identify a top-per-group student.

  Data Manipulation (SQL/Python)Technical Screen
• RRbcroyalbankEasy·Data Engineer
  Explain pandas and SQL basics

  This question evaluates familiarity with pandas data structures (Series vs DataFrame), SQL filtering semantics (WHERE vs HAVING), and practical SQL aggregation for detecting duplicate records, assessing competencies in data manipulation, aggregation, and basic data quality checks.

  Data Manipulation (SQL/Python)Technical Screen
• RedditMedium·Data Scientist
  Query US-based users and most active forums

  This question evaluates SQL data manipulation skills, including time-based filtering, joins, aggregations, null-safe handling, ratio computation, distinct counts, and top-N ranking to determine user residency and forum activity.

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• RipplingMedium·Data Analyst
  Find Payroll Bookings in the Latest Active Month by Country

  Tests PostgreSQL date manipulation, filtering, and aggregation by determining the most recent month with active payroll bookings and summarizing totals by country.

  Data Manipulation (SQL/Python)Technical Screensqlpostgresqldate-manipulation
• RobinhoodMedium·Data Scientist
  Identify Top Users with Declined Transactions in SQL

  This question evaluates proficiency in SQL data manipulation, specifically aggregation, window functions, rolling 24-hour time-window analytics, NULL handling, and conditional classification via CASE WHEN within a fraud/risk detection scenario.

  Data Manipulation (SQL/Python)Technical Screen
• RobinhoodMedium·Data Scientist
  Identify Transactions During 'Golden' Membership Period

  This question evaluates proficiency in temporal data manipulation and aggregation, covering SQL date-range joins, window functions for per-user ranking, and Python frequency counting of senders and receivers, and it falls under the Data Manipulation (SQL/Python) domain for data scientist roles.

  Data Manipulation (SQL/Python)Technical Screen
• RobinhoodMedium·Data Scientist
  List Transactions During Active 'Gold' Membership Period

  This question evaluates proficiency in SQL-based data manipulation and temporal reasoning, specifically the ability to correlate membership periods with transaction records using relational operations.

  Data Manipulation (SQL/Python)Technical Screen
• RobinhoodMedium·Data Scientist
  Write SQL and Python for transaction analytics

  This question evaluates data manipulation competency in SQL and Python, covering normalization of transaction direction, JOINs and CASE logic, window functions for ranking and aggregation, and designing single-pass counting algorithms for streaming-like inputs.

  Data Manipulation (SQL/Python)Technical Screen
• RobinhoodMedium·Data Scientist
  Write SQL to localize trading drop contributors

  This question evaluates proficiency in SQL-based data manipulation and analytics, including cohorting by signup month, daily aggregation of executed trades per active user, segmentation by platform and geolocation, and competency with joins, timestamps, and operational flags.

  Data Manipulation (SQL/Python)Technical Screen
• RobloxMedium·Data Scientist
  Compare ROW_NUMBER, RANK, and DENSE_RANK for Top-k Results

  Compare ROW_NUMBER, RANK, and DENSE_RANK with ties, exact-row limits, distinct score levels, and deterministic top-k semantics.

  Data Manipulation (SQL/Python)Technical Screen
• RobloxMedium·Data Scientist
  Compute ARPDAU/ARPPU by country

  This question evaluates data manipulation and analytics skills for computing user- and revenue-based metrics (ARPDAU, ARPPU), focusing on aggregation, multi-key joins, deduplication, time-window filtering, and correct revenue attribution.

  Data Manipulation (SQL/Python)Technical Screen
• RobloxMedium·Data Scientist
  Write SQL for ads metrics and variability

  This question evaluates a candidate's ability to perform SQL-based data manipulation and analytics, including deduplication of events, computation of daily and campaign-level metrics (CTR, CPC), and calculation of statistical summaries (mean and standard deviation) and outlier detection over a specified date range.

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• RobloxEasy·Data Scientist
  Write SQL for influence score and follower growth

  This question evaluates SQL data manipulation competencies such as joins, aggregations, distinct counts, window functions, and time-based grouping for computing an influence score (engagements on a user's posts plus follower count) and monthly follower growth, and it falls under Data Manipulation (SQL/Python).

  Data Manipulation (SQL/Python)Technical Screen
• RRokuMedium·Data Scientist
  Write SQL for profit, growth, retention

  This question evaluates a candidate's ability to perform advanced SQL data manipulation — specifically rolling-window aggregations, revenue and gross profit computations that account for refunds, order status and test flags, plus cohort retention and week-over-week growth analysis.

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• Scale AIMedium·Machine Learning Engineer
  Debug ML pipeline and build text parser

  This question evaluates practical machine learning engineering skills, including debugging unfamiliar codebases, parsing noisy text data, and identifying subtle defects such as train/test leakage or shape mismatches. It tests applied Python proficiency and systematic reasoning under time pressure, core competencies assessed in ML engineer technical interviews.

  Data Manipulation (SQL/Python)Technical Screen
• ShopifyMedium·Data Scientist
  Analyze Pirate Theme Usage Growth Over Time

  This question evaluates a candidate's ability to perform SQL-based data manipulation and time-series analysis, emphasizing joins, aggregation, time-bucketing, and definition of growth metrics to measure feature adoption.

  Data Manipulation (SQL/Python)Technical Screen
• ShopifyMedium·Data Scientist
  Analyze Pirated Theme Usage

  Calculate monthly metrics on unlicensed theme usage and lost revenue by evaluating temporal interval overlaps between installation lifespans and calendar months.

  Data Manipulation (SQL/Python)Technical ScreenSQLPandasInterval Overlap
• ShopifyMedium·Data Scientist
  Analyze Pirated Theme Usage Impact

  This question evaluates a candidate's ability to perform data manipulation and analytics with SQL and Python to detect license compliance, construct month-level time windows, and quantify revenue impact from pirated theme usage.

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• ShopifyMedium·Data Scientist
  Calculate Week-over-Week Revenue Change Using SQL

  This question evaluates proficiency in SQL time-series aggregation, weekly grouping, and prior-period comparison for calculating percentage changes in revenue.

  Data Manipulation (SQL/Python)Technical Screen
• ShopifyMedium·Data Scientist
  Measure Pirate-Theme Usage and Revenue Growth Over Time

  This question evaluates the ability to design time-based usage metrics and compute monthly revenue through data manipulation using SQL or Python, focusing on aggregating event and transaction records by calendar month.

  Data Manipulation (SQL/Python)Technical Screen
• SnapchatMedium·Data Scientist
  Calculate Average Camera Switches per Session

  Tests the ability to write a PostgreSQL query using window functions (LAG) to compute the average number of specific state transitions per session while accounting for zero-transition sessions and avoiding integer division.

  Data Manipulation (SQL/Python)Technical ScreenSQLPostgreSQLWindow Functions
• SnapchatMedium·Data Scientist
  Compute same-day acceptance metrics last week

  This question evaluates proficiency in time-based data manipulation, SQL JOINs and aggregations, handling NULLs and missing reference rows, and computing acceptance and percentage metrics with careful UTC date boundary treatment.

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• SnapchatMedium·Data Scientist
  Count Users Whose First Daily Event Is App Launch

  Write a PostgreSQL query to identify each user's first event per calendar day and aggregate the distinct user count by operating system and device when that first event is an app launch. Tests window functions, tie-breaking logic, and multi-dimensional grouping.

  Data Manipulation (SQL/Python)Technical ScreenSQLPostgreSQLWindow Functions
• SnowflakeMedium·Data Scientist
  Query seven-day conversion with windows and dedupe

  This question evaluates proficiency in time-windowed aggregations, event deduplication, and window-function–based user-level analytics across SQL and Pandas workflows.

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• SpotifyEasy·Machine Learning Engineer
  Query Top Played Tracks Globally And By Country

  Tests writing clean SQL queries with aggregations, window functions, and deterministic tie-breaking to extract top playback metrics globally and per country.

  Data Manipulation (SQL/Python)Technical Screensqlwindow-functionsaggregation
• StripeMedium·Software Engineer
  Compute costs with validation and sorting in Python

  This question evaluates proficiency in data validation, numeric robustness, error handling, aggregation, per-item cost computation and deterministic sorting within Python-based data manipulation tasks, and it belongs to the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Technical Screen