Data Manipulation (SQL/Python) interview questions

Showing 91–120 of 446 results

• Calculate Longest Transaction Streak for Each UserMedium

  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.

  Capital OneData ScientistData Manipulation (SQL/Python)Technical Screen
• Clean and Merge Housing DataEasy

  This question tests the ability to clean messy housing datasets, handle inconsistent data types, and perform efficient left-joins with external reference data.

  Capital OneData ScientistData Manipulation (SQL/Python)Onsite
• Compute Customer Spend and Engineer Features for 2023Medium

  This question evaluates proficiency in SQL-based temporal aggregation and Python feature engineering on transactional datasets, including computing monthly spend summaries and engineering category-level spending features.

  Capital OneData ScientistData Manipulation (SQL/Python)Onsite
• Determine Country with Most 'Sunny' DaysMedium

  This question evaluates proficiency in data aggregation, grouping, and filtering to summarize categorical time-series records using SQL or equivalent Python data-manipulation tools, and it belongs to the Data Manipulation domain with an emphasis on practical application of query and aggregation techniques.

  Capital OneData ScientistData Manipulation (SQL/Python)Onsite
• Find top category per region in Aug 2025Medium

  This question evaluates proficiency in SQL data manipulation skills such as joining related tables, aggregating revenue, filtering by date and status, and implementing deterministic tie-breaking for categories.

  Capital OneData ScientistData Manipulation (SQL/Python)Online Assessment
• Fix dash dates and aggregate watch timeMedium

  This question evaluates data-cleaning and aggregation competencies, focusing on handling nonstandard missing-date markers, forward-filling in spreadsheets, correct date-range filtering, pivot-table summarization, and concise pandas grouping and aggregation.

  Capital OneData ScientistData Manipulation (SQL/Python)Online Assessment
• Impute missing values without leakageMedium

  This question evaluates proficiency in data imputation, strict train/validation leakage prevention, temporal per-user propagation rules, and categorical aggregation for model-ready features.

  Capital OneData ScientistData Manipulation (SQL/Python)HR Screen
• Merge CSVs and build revenue pivot with pandasMedium

  This question evaluates proficiency in data manipulation and ETL using pandas, covering robust file I/O, join/merge semantics, null/coalescing behavior, date parsing, aggregation, and pivoting.

  Capital OneData ScientistData Manipulation (SQL/Python)Online Assessment
• Merge ad CSVs and compute CTRMedium

  This question evaluates SQL data manipulation competencies such as cleaning and merging CSVs, forward-filling missing dates, correct multi-table joins without duplication, use of window functions, time-windowed aggregations, and computation of click-through rate (CTR).

  Capital OneData ScientistData Manipulation (SQL/Python)Online Assessment
• Merge seven tables into one clean DataFrameMedium

  This question evaluates proficiency with pandas-based data manipulation, including multi-table joins, aggregations, dtype enforcement, denormalization, and verification of row/column integrity within the Data Manipulation (SQL/Python) domain for a Data Scientist role.

  Capital OneData ScientistData Manipulation (SQL/Python)HR Screen
• Reconcile ledgers with SQL/Python and late eventsMedium

  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.

  Capital OneData ScientistData Manipulation (SQL/Python)Technical Screen
• Write SQL for lowest price with ratingsMedium

  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.

  Capital OneData ScientistData Manipulation (SQL/Python)Technical Screen
• Write SQL for theme-park revenue and visitsMedium

  This question evaluates SQL data-manipulation and analytical skills, including joins, aggregations, date-range filtering, handling multiple ticket ownership, and edge cases like zero counts when computing revenue and visit metrics.

  Capital OneData ScientistData Manipulation (SQL/Python)Onsite
• Write SQL to compute campaign net revenueMedium

  This question evaluates SQL-based data manipulation and analytics skills, including aggregation, deduplication, date-range filtering, revenue and cost calculations, and selection logic for prioritizing additional donors.

  Capital OneData ScientistData Manipulation (SQL/Python)Onsite
• Write SQL to find top net-revenue productsMedium

  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.

  Capital OneData ScientistData Manipulation (SQL/Python)Technical Screen
• Write SQL for character safety and engagement metricsMedium

  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.

  Character.AIData ScientistData Manipulation (SQL/Python)Technical Screen
• Analyze Acquisition Channels for User Value and RetentionMedium

  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.

  ChimeData ScientistData Manipulation (SQL/Python)Technical Screen
• Define and Analyze Early EngagementMedium

  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.

  ChimeData ScientistData Manipulation (SQL/Python)Technical Screen
• Write SQL for noisy A/B launch metricsMedium

  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.

  ChimeData ScientistData Manipulation (SQL/Python)Technical Screen
• Write rolling-window SQL over weekly cohortsMedium

  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.

  ChimeData ScientistData Manipulation (SQL/Python)Technical Screen
• Write PostgreSQL updates with date filtersMedium

  This question evaluates competency in PostgreSQL data manipulation, covering updates with correct timestamp handling, upserts with conflict resolution, date-range selection using timestamptz, and index design plus performance analysis with EXPLAIN ANALYZE.

  CircleSoftware EngineerData Manipulation (SQL/Python)Online Assessment
• Answer SQL And Data Warehouse Fundamentals For A Data Engineering InterviewMedium

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

  ClickhouseData EngineerData Manipulation (SQL/Python)Technical Screen
• Calculate Average Rate and Cumulative Jobs by StateMedium

  This question evaluates competency in relational data manipulation and analytical SQL concepts—including joins, aggregation with DISTINCT counts, per-date averaging, rolling cumulative counts, and handling numeric edge cases—within the Data Manipulation (SQL/Python) domain for a data scientist role.

  CloudTrucksData ScientistData Manipulation (SQL/Python)Technical Screen
• Compute adoption, latency, and cross-region transactionsMedium

  This question evaluates proficiency in SQL-based data manipulation and analytics, including calculating cohort adoption and transaction rates, performing date arithmetic for time-to-first-transaction, identifying cross-region transactions, and computing distributional percentiles.

  CoinbaseData ScientistData Manipulation (SQL/Python)Onsite
• Compute delivery metrics and top-K queriesMedium

  Compute delivery metrics and top-K queries 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.

  CoinbaseSoftware EngineerData Manipulation (SQL/Python)Onsite
• Implement filters and cursor paginationMedium

  This question evaluates a candidate's ability to design and implement cursor-based pagination, conjunctive per-field filtering, stable sort key selection, API surface design, and complexity analysis for a database-backed transaction query module.

  CoinbaseSoftware EngineerData Manipulation (SQL/Python)Onsite
• Write SQL and Python for funnels/retentionMedium

  This question evaluates SQL and Python data-manipulation skills, including event-to-session joins, temporal window filtering, deduplication, JSON metadata handling, and computation of funnel and retention metrics like daily unique users and conversion rates.

  CoinbaseData ScientistData Manipulation (SQL/Python)HR Screen
• Write SQL: sum values ≤ each row’s valueMedium

  This question evaluates proficiency in SQL aggregation, query optimization, and handling duplicate values when computing cumulative sums across rows. It is commonly asked to assess practical ability in the Data Manipulation (SQL/Python) domain to design scalable, efficient queries and demonstrates a focus on practical application of query-design and optimization rather than purely conceptual theory.

  CoinbaseData ScientistData Manipulation (SQL/Python)Onsite
• Determine Tutorial Completion Rate by Variant in 7 DaysMedium

  This question evaluates a candidate's competency in data manipulation and event-based analytics, focusing on skills such as joining user and event tables, aggregating event data, and computing cohort completion metrics by variant.

  ConfluentData ScientistData Manipulation (SQL/Python)Onsite
• Monthly Cohort RetentionMedium

  This question tests the ability to transform raw user activity logs into a cohort retention matrix by calculating the time elapsed between a user's first interaction and subsequent activity.

  CoupangData ScientistData Manipulation (SQL/Python)Technical Screen