Data Manipulation (SQL/Python) Interview Questions

Showing 31–60 of 256 results

• AmazonMedium·Data Scientist
  Verify subscriptions and analyze orders with SQL/Python

  This question evaluates proficiency in SQL and pandas data manipulation, covering data-quality validation, temporal sequence reasoning, deduplication, deterministic aggregation, and tie-breaking logic.

  Data Manipulation (SQL/Python)Technical Screen
• AppleMedium·Software Engineer
  Explain Python lists, dicts, and concurrency

  Explain Python lists, dicts, and concurrency 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.

  Data Manipulation (SQL/Python)Technical Screen
• AtlassianMedium·Data Scientist
  Find 2023 NCAA championship winner

  This question evaluates proficiency in SQL data manipulation and relational reasoning, testing the ability to identify a championship winner from game result records across related tables in the Data Manipulation (SQL/Python) domain for data scientist roles.

  Data Manipulation (SQL/Python)Technical Screen
• BRBilt RewardsMedium·Software Engineer
  Write SQL to Join Merchants and Triple Restaurant Points

  This question evaluates SQL querying and data engineering competencies—specifically JOINs, conditional logic (CASE), ETL staging, data cleaning, indexing, and approximate string matching—within the data manipulation and database domain and emphasizes practical application of SQL for transforming transactional datasets.

  Data Manipulation (SQL/Python)Technical Screen
• B(Block (Square)Medium·Data Scientist
  Write left-join queries with tricky filters

  This question evaluates SQL data-manipulation skills including LEFT JOIN semantics, aggregation functions like COUNT(DISTINCT) and SUM, GROUP BY/HAVING logic, and date-window filtering for referral and order analytics.

  Data Manipulation (SQL/Python)Technical Screen
• BloombergMedium·Data Engineer
  Write PostgreSQL string-manipulation query

  This question evaluates proficiency in PostgreSQL string manipulation, regular expressions, URL parsing, key-value parsing and timestamp-based filtering for data transformation tasks.

  Data Manipulation (SQL/Python)Technical Screen
• Boston Consulting GroupMedium·Data Scientist
  Manipulate and merge DataFrames correctly

  This question evaluates proficiency in pandas-based data manipulation and merging, covering join validation, vectorized revenue and discount calculations, missing-value imputation, groupby/transform usage, dtype setting, and avoiding SettingWithCopy issues.

  Data Manipulation (SQL/Python)Technical Screen
• Boston Consulting GroupMedium·Data Scientist
  Query top spenders and 7-day growth

  This question evaluates proficiency in SQL data manipulation, including aggregations, date-range filtering, joins, window functions for ranking and tie-breaking, and computing revenue shares and growth rates within a relational schema.

  Data Manipulation (SQL/Python)Technical Screen
• Boston Consulting GroupMedium·Data Scientist
  Transform messy transactions with pandas

  This question evaluates proficiency in large-scale data manipulation and feature engineering using pandas, covering timestamp normalization across time zones, deduplication and last-write-wins semantics, data validation, memory-efficient aggregation, rolling-window statistics, and top-k categorical extraction for merchant categories.

  Data Manipulation (SQL/Python)Technical Screen
• Boston Consulting GroupMedium·Data Scientist
  Visualize and Clean SKU Sales Data for Outliers

  This question evaluates data manipulation and exploratory data analysis skills, including aggregation, outlier detection (IQR/Z-score) and visualization using Python libraries such as pandas and matplotlib/seaborn.

  Data Manipulation (SQL/Python)Technical Screen
• BByteDanceMedium·Data Scientist
  Campaign Efficiency Metrics and Below-Average CTR

  Practice a data scientist SQL interview question that calculates campaign cost and funnel rates, handles zero denominators, and filters campaigns against an average CTR benchmark. The exercise tests PostgreSQL date arithmetic, CTE organization, NULL-safe calculations, and deterministic output.

  Data Manipulation (SQL/Python)Technical Screen
• BByteDanceMedium·Data Scientist
  Compare SQL counts, windows, and NULL semantics

  This question evaluates understanding of SQL aggregation and NULL semantics, window functions (ROW_NUMBER vs RANK), join behavior to avoid double-counting, and defensive handling of NULLs and divide-by-zero using COALESCE and NULLIF in a data manipulation context.

  Data Manipulation (SQL/Python)Technical Screen
• BByteDanceMedium·Data Scientist
  Find Each User's Earliest Seven-Day Activity Streak

  Tests your ability to identify consecutive date sequences (gaps-and-islands problem) in PostgreSQL, aggregate streaks, and filter for each user's earliest qualifying period.

  Data Manipulation (SQL/Python)Technical Screensqlpostgresqlwindow-functions
• BByteDanceMedium·Data Scientist
  Find top-paid employee per department

  This problem tests your ability to perform group-wise filtering and ranking using window functions or joins to extract top-performing records within categories.

  Data Manipulation (SQL/Python)Technical ScreenSQLWindow FunctionsAggregation
• BByteDanceHard·Backend Engineer
  SQL: Students Above Average and Passing Every Subject

  This problem tests advanced SQL skills, including the use of window functions, conditional aggregation, and multi-level filtering to identify subsets of data that satisfy both global and per-category criteria.

  Data Manipulation (SQL/Python)Technical ScreenSQLAggregationWindow-Functions
• CVS HealthMedium·Data Scientist
  Aggregate radiology spend and derive fiscal month

  This question evaluates a candidate's competency in data manipulation and preprocessing using SQL/Python, covering robust dtype specification, null handling, numeric aggregation and percentage computation, datetime parsing, and fiscal-month derivation.

  Data Manipulation (SQL/Python)Technical Screen
• CVS HealthMedium·Data Scientist
  Calculate annual percentages and YoY by cohorts

  This question evaluates SQL and pandas data manipulation skills, specifically joins, conditional aggregation, deduplication, cohort-based percentage computations, and year-over-year change calculations.

  Data Manipulation (SQL/Python)Technical Screen
• CVS HealthEasy·Data Scientist
  Compute specialty spend share and top age band

  This question evaluates data manipulation and aggregation skills in SQL and Pandas, including joins/unions across disjoint member tables, computation of specialty spend shares, identification of top age bands by claim counts with tie handling, year-based date filtering, and categorical recoding with monthly grouping.

  Data Manipulation (SQL/Python)Technical Screen
• CVS HealthMedium·Data Scientist
  Use pandas to aggregate, pivot, and label

  This question evaluates competency in pandas-based tabular data manipulation—specifically DataFrame merging, groupby aggregation, pivot table construction, counting unique values, and conditional flagging with numpy—within the domain of Data Manipulation (SQL/Python) and is primarily at the practical application level.

  Data Manipulation (SQL/Python)Technical Screen
• CVS HealthMedium·Data Scientist
  Write SQL for dedup and purchase shares

  This question evaluates proficiency in SQL data manipulation skills including entity deduplication, canonicalization rules, window functions, common table expressions, joins, and aggregation to derive per-user purchase counts and distributions.

  Data Manipulation (SQL/Python)Technical Screen
• Capital OneMedium·Data Scientist
  Calculate Longest Transaction Streak for Each User

  This question evaluates proficiency in data manipulation and time-series sequence analysis, specifically measuring the longest run of consecutive successful transactions per user from transactional logs.

  Data Manipulation (SQL/Python)Technical Screen
• Capital OneMedium·Data Scientist
  Reconcile ledgers with SQL/Python and late events

  This question evaluates a candidate's ability to implement data reconciliation and ETL engineering skills, including SQL window functions and joins, time-based FX rate lookups, matching logic for late-arriving events, idempotent daily pipeline design with deterministic keys and exactly-once semantics, and basic statistical charting in Python.

  Data Manipulation (SQL/Python)Technical Screen
• Capital OneMedium·Data Scientist
  Write SQL for lowest price with ratings

  This question evaluates SQL data-manipulation skills including aggregation, conditional filtering, NULL handling, grouping, and deterministic ordering, and sits in the Data Manipulation (SQL/Python) domain with an emphasis on practical application of query writing rather than purely conceptual design.

  Data Manipulation (SQL/Python)Technical Screen
• Capital OneMedium·Data Scientist
  Write SQL to find top net-revenue products

  This question evaluates proficiency in SQL data manipulation, including joins, aggregations, date-window filtering, handling returns and net-revenue calculations, plus the use of analytic/window functions for per-category ranking and tie-breaking.

  Data Manipulation (SQL/Python)Technical Screen
• CCharacter.AIMedium·Data Scientist
  Write SQL for character safety and engagement metrics

  This question evaluates proficiency in SQL-based data manipulation and analytics, specifically aggregations, joins, date-based grouping, ratio calculations, and windowed moving averages to compute safety and engagement metrics.

  Data Manipulation (SQL/Python)Technical Screen
• ChimeMedium·Data Scientist
  Analyze Acquisition Channels for User Value and Retention

  This question evaluates proficiency in SQL/Python data manipulation and analytics, testing aggregation, cohort and retention analysis, ranking, joins and window-function usage along with interpretation of user-level revenue metrics.

  Data Manipulation (SQL/Python)Technical Screen
• ChimeMedium·Data Scientist
  Define and Analyze Early Engagement

  Define an early user activation metric for a fintech platform, write a SQL query to measure monthly cohort activation rates across acquisition channels, and account for lifecycle maturity bias.

  Data Manipulation (SQL/Python)Technical Screensqlcohort-analysisfintech
• ChimeMedium·Data Scientist
  Write SQL for noisy A/B launch metrics

  This question evaluates SQL-based data manipulation and experimental analytics skills, including attribution of earliest exposures, censoring crossovers, session-based bot qualification, time-windowed conversion and revenue calculations, and exclusion of outage periods in PostgreSQL.

  Data Manipulation (SQL/Python)Technical Screen
• ChimeMedium·Data Scientist
  Write rolling-window SQL over weekly cohorts

  This question evaluates proficiency with SQL window functions and CTEs for time-based cohort aggregation, including rolling ROWS-based sums, LAG-based week-over-week percent change, DENSE_RANK ranking, and handling first- and second-purchase calculations.

  Data Manipulation (SQL/Python)Technical Screen
• CClickhouseMedium·Data Engineer
  Answer SQL And Data Warehouse Fundamentals For A Data Engineering Interview

  This problem tests foundational knowledge of SQL set operations, performance optimization techniques for join operations, and the architectural principles behind building scalable data warehouses.

  Data Manipulation (SQL/Python)Technical ScreenSQLData WarehousingWindow Functions