Data Manipulation (SQL/Python) interview questions

Showing 61–90 of 446 results

• Verify subscriptions and analyze orders with SQL/PythonMedium

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

  AmazonData ScientistData Manipulation (SQL/Python)Technical Screen
• Explain Python lists, dicts, and concurrencyMedium

  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.

  AppleSoftware EngineerData Manipulation (SQL/Python)Technical Screen
• Write queries to compute salary and budget statsEasy

  This set of tasks evaluates data manipulation and analytical competencies, specifically the ability to compute order-statistics on numeric columns, perform relational joins to enrich event records, and reason about constrained selection for maximizing hires using SQL and pandas.

  AppleData ScientistData Manipulation (SQL/Python)Onsite
• Implement a nested object validatorMedium

  This question evaluates proficiency in nested data validation, schema interpretation, and type checking for hierarchical objects, including handling nulls, arrays, extra fields, and clear error reporting.

  Applied IntuitionSoftware EngineerData Manipulation (SQL/Python)Onsite
• Find 2023 NCAA championship winnerMedium

  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.

  AtlassianData ScientistData Manipulation (SQL/Python)Technical Screen
• Write SQL to Join Merchants and Triple Restaurant PointsMedium

  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.

  Bilt RewardsSoftware EngineerData Manipulation (SQL/Python)Technical Screen
• Write left-join queries with tricky filtersMedium

  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.

  Block (Square)Data ScientistData Manipulation (SQL/Python)Technical Screen
• Write PostgreSQL string-manipulation queryMedium

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

  BloombergData EngineerData Manipulation (SQL/Python)Technical Screen
• Manipulate and merge DataFrames correctlyMedium

  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.

  Boston Consulting GroupData ScientistData Manipulation (SQL/Python)Technical Screen
• Merge and Clean Customer Order Data for AnalysisMedium

  This question evaluates proficiency in data manipulation with pandas and SQL concepts, focusing on dataset merging, missing-value handling, and computing derived aggregate columns such as total_spent.

  Boston Consulting GroupData ScientistData Manipulation (SQL/Python)Online Assessment
• Merge and Concatenate Inconsistent Order Files with PandasMedium

  This question evaluates proficiency with pandas-based data manipulation, including schema alignment, column renaming, type casting, and vertical concatenation when merging CSV files.

  Boston Consulting GroupData ScientistData Manipulation (SQL/Python)Online Assessment
• Query top spenders and 7-day growthMedium

  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.

  Boston Consulting GroupData ScientistData Manipulation (SQL/Python)Technical Screen
• Transform messy transactions with pandasMedium

  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.

  Boston Consulting GroupData ScientistData Manipulation (SQL/Python)Technical Screen
• Visualize and Clean SKU Sales Data for OutliersMedium

  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.

  Boston Consulting GroupData ScientistData Manipulation (SQL/Python)Technical Screen
• Campaign Efficiency Metrics and Below-Average CTRMedium

  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.

  ByteDanceData ScientistData Manipulation (SQL/Python)Technical Screen
• Compare SQL counts, windows, and NULL semanticsMedium

  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.

  ByteDanceData ScientistData Manipulation (SQL/Python)Technical Screen
• Find Each User's Earliest Seven-Day Activity StreakMedium

  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.

  ByteDanceData ScientistData Manipulation (SQL/Python)Technical Screen
• Find top-paid employee per departmentMedium

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

  ByteDanceData ScientistData Manipulation (SQL/Python)Technical Screen
• Model Data for Analytics, Reporting, and ApplicationsMedium

  Tests your ability to process raw semi-structured event logs into a clean, dimensional metric table suitable for downstream analytics and reporting.

  ByteDanceData EngineerData Manipulation (SQL/Python)HR Screen
• SQL: Students Above Average and Passing Every SubjectHard

  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.

  ByteDanceBackend EngineerData Manipulation (SQL/Python)Technical Screen
• Aggregate radiology spend and derive fiscal monthMedium

  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.

  CVS HealthData ScientistData Manipulation (SQL/Python)Technical Screen
• Calculate annual percentages and YoY by cohortsMedium

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

  CVS HealthData ScientistData Manipulation (SQL/Python)Technical Screen
• Compute specialty spend share and top age bandEasy

  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.

  CVS HealthData ScientistData Manipulation (SQL/Python)Technical Screen
• Create Views, Insert, and Update CorrectlyMedium

  This question evaluates SQL data-manipulation competencies including view creation, aggregate joins, INSERT/UPDATE operations, and reasoning about temporal effects on computed aggregates such as historical customer spend.

  CVS HealthData ScientistData Manipulation (SQL/Python)Online Assessment
• Create and query an e-commerce schemaMedium

  This question evaluates SQL schema design and data manipulation competencies, including defining PostgreSQL table types and constraints, inserting exact datasets, composing aggregate and join queries with date-based filters, and performing DML updates with verification.

  CVS HealthData ScientistData Manipulation (SQL/Python)Online Assessment
• Use pandas to aggregate, pivot, and labelMedium

  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.

  CVS HealthData ScientistData Manipulation (SQL/Python)Technical Screen
• Write SQL for dedup and purchase sharesMedium

  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.

  CVS HealthData ScientistData Manipulation (SQL/Python)Technical Screen
• Aggregate exam scores with NULL handlingMedium

  This question evaluates proficiency in SQL data manipulation, specifically joins (including LEFT JOIN behavior), NULL handling with ISNULL/COALESCE, conditional aggregation, distinct counts, and date-based filtering within the Data Manipulation (SQL/Python) domain, focusing on practical application of query-writing skills.

  Capital OneData ScientistData Manipulation (SQL/Python)Online Assessment
• Analyze Mission Outcomes and Allocate Response UnitsEasy

  Analyze incident response records to calculate unit win rates, evaluate the impact of rest periods on success, and rank response units using tie-breakers.

  Capital OneData AnalystData Manipulation (SQL/Python)Online Assessment
• Audit flight data quality from metadataMedium

  This question evaluates data quality auditing competencies, including validation of timestamps and timezones, detection of label leakage and inconsistent units, provenance and schema enforcement, and hands‑on use of SQL/Python for dataset inspection.

  Capital OneData ScientistData Manipulation (SQL/Python)Onsite