Data Manipulation (SQL/Python) Interview Questions

Showing 31–60 of 134 results

• Capital OneMedium·Data Scientist
  Write SQL for theme-park revenue and visits

  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.

  Data Manipulation (SQL/Python)Onsite
• Capital OneMedium·Data Scientist
  Write SQL to compute campaign net revenue

  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.

  Data Manipulation (SQL/Python)Onsite
• CoinbaseMedium·Data Scientist
  Compute adoption, latency, and cross-region transactions

  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.

  Data Manipulation (SQL/Python)Onsite
• CoinbaseMedium·Software Engineer
  Compute delivery metrics and top-K queries

  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.

  Data Manipulation (SQL/Python)Onsite
• CoinbaseMedium·Software Engineer
  Implement filters and cursor pagination

  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.

  Data Manipulation (SQL/Python)Onsite
• CoinbaseMedium·Data Scientist
  Write SQL: sum values ≤ each row’s value

  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.

  Data Manipulation (SQL/Python)Onsite
• ConfluentMedium·Data Scientist
  Determine Tutorial Completion Rate by Variant in 7 Days

  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.

  Data Manipulation (SQL/Python)Onsite
• DDaveEasy·Machine Learning Engineer
  Parse JSON Accounts into Typed Python Objects and Prepare Features

  This problem tests your ability to sanitize and transform raw JSON account data into a structured format while enforcing strict numerical integrity and data consistency.

  Data Manipulation (SQL/Python)OnsitePythonData CleaningJSON
• DoorDashHard·Data Scientist
  Calculate Order Request Metrics

  This question evaluates SQL and data-manipulation competencies for computing order-level metrics, including aggregations, joins, windowed event sequencing, timestamp/timezone handling, and NULL-aware comparisons.

  Data Manipulation (SQL/Python)Onsite
• DoorDashMedium·Software Engineer
  Compute dasher pay from deliveries

  This question evaluates competency in data manipulation, schema design and validation, time-based aggregation, pay-computation logic, and handling of edge cases using SQL or Python.

  Data Manipulation (SQL/Python)Onsite
• DoorDashMedium·Software Engineer
  Compute dasher payout from API data

  This question evaluates a candidate's proficiency in data manipulation and transaction aggregation using SQL or Python, encompassing time-zone-aware timestamp handling, pagination, late or missing events, cancellations and negative adjustments, rounding rules, and itemized breakdowns.

  Data Manipulation (SQL/Python)Onsite
• DoorDashMedium·Data Scientist
  Compute rolling cold-delivery rates with windows

  This question evaluates proficiency with SQL window functions, time-based joins, event deduplication and business-rule implementation for computing rolling metrics over temporal windows.

  Data Manipulation (SQL/Python)Onsite
• DoorDashMedium·Data Scientist
  Model schema and query new-market readiness

  This question evaluates SQL-based data manipulation skills including time-series aggregation, cohort analysis, percentile/median calculations, joins between event and reference tables, and deriving operational KPIs for new-market readiness; it targets the Data Manipulation (SQL/Python) domain for a Data Scientist role.

  Data Manipulation (SQL/Python)Onsite
• DoorDashMedium·Data Scientist
  Write complex SQL on DoorDash data

  This question evaluates proficiency in complex SQL querying and data manipulation, including joins across tables, time-based filtering, window functions, null and edge-case handling, aggregations, and integrating promotion and order-event data.

  Data Manipulation (SQL/Python)Onsite
• ExpediaMedium·Data Scientist
  Audit and onboard unfamiliar datasets safely

  This question evaluates a data scientist's competency in dataset discovery, schema and key verification, profiling for distributions/missingness/timezones/currencies, duplicate and join-inflation detection, event-sequence validation, accurate metric computation, and the design of data-quality tests/data contracts within SQL/Python workflows.

  Data Manipulation (SQL/Python)Onsite
• FMFannie MaeMedium·Data Scientist
  Understand SQL Aggregations and Joins: Key Differences Explained

  This question evaluates a candidate's competency in SQL aggregations (COUNT, SUM, AVG, MIN, MAX), join semantics (INNER, LEFT, RIGHT, FULL OUTER), set operations (UNION vs UNION ALL), window functions, view versus table trade-offs, duplicate detection and removal, query optimization techniques, and pandas DataFrame operations (merge, join, concat) within the Data Manipulation (SQL/Python) domain. It is commonly asked because it assesses both conceptual understanding (function purposes and join semantics) and practical application (writing efficient queries, deduplicating data, and translating patterns between SQL and pandas), thereby probing correctness and performance reasoning.

  Data Manipulation (SQL/Python)Onsite
• Freddie MacMedium·Data Scientist
  Compute leakage-safe rolling features in pandas

  This question evaluates proficiency in time-series feature engineering, leakage prevention, memory-efficient large-scale data processing, categorical target encoding, exponential weighting, and algorithmic complexity within the Data Manipulation (SQL/Python) domain, emphasizing practical application with pandas at scale.

  Data Manipulation (SQL/Python)Onsite
• GleanMedium·Data Scientist
  SQL: Zero-Filled DAU, Rolling 30-Day MAU, and the MAU Impact of a User-ID Rehash

  Three SQL questions on a login table: daily active users with zero-filled days, rolling 30-day MAU per date with a weekday-usage follow-up, and how a user-ID rehash inflates MAU. Tests distinct counting, calendar generation, window boundaries, query cost and reasoning about metric artifacts.

  Data Manipulation (SQL/Python)Onsite
• GoogleMedium·Data Scientist
  Calculate Top Countries' Gmail Usage and MoM Change

  This question evaluates proficiency in SQL data aggregation, time-based grouping, and analytical window functions for computing month-over-month percentage changes in event volumes.

  Data Manipulation (SQL/Python)Onsite
• HDHome DepotEasy·Product Analyst
  Debug SQL join that drops rows

  This question evaluates understanding of SQL joins, filtering interactions, aggregation, and troubleshooting data discrepancies when combining sales, sale_items, products, and promotions tables.

  Data Manipulation (SQL/Python)Onsite
• HDHome DepotMedium·Data Analyst
  Debug row loss after SQL joins

  This question evaluates SQL data manipulation and JOIN logic skills, including temporal and timezone-aware date handling and aggregation, within the Data Manipulation (SQL/Python) domain and primarily tests practical application-level competency.

  Data Manipulation (SQL/Python)Onsite
• InstacartMedium·Software Engineer
  Implement a pivot table transformation

  This question evaluates competency in data transformation and aggregation, specifically pivot operations, time-based grouping, null handling, dimensionality reduction (top‑K/OTHER), scalability for large datasets, and expressing solutions in both Python (pandas or pure) and SQL within the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Onsite
• InstacartMedium·Software Engineer
  Pivot data without date libraries

  This question evaluates a candidate's competency in data manipulation and transformation, specifically pivoting tall datasets into wide views, handling missing metric values, ordering ISO date strings via string operations, and meeting algorithmic efficiency constraints (O(n log n)).

  Data Manipulation (SQL/Python)Onsite
• InstacartMedium·Software Engineer
  Pivot transactions by date without date libs

  This question evaluates proficiency in data manipulation, string-based date handling, aggregation and pivot transformations, along with reasoning about data structures, de-duplication and missing-value filling.

  Data Manipulation (SQL/Python)Onsite
• LyftMedium·Data Scientist
  Query and transform marketplace data in SQL/Python

  This question evaluates mastery of data manipulation and analytics skills in the Data Manipulation (SQL/Python) domain, including SQL aggregation and window functions, time-based joins and grouping, deduplication of status streams, handling late-arriving events, and implementing rolling metrics using both SQL and pandas.

  Data Manipulation (SQL/Python)Onsite
• MWMarshall WaceMedium·Software Engineer
  Find users with multi-country successful logins

  This question evaluates proficiency in relational data manipulation, text normalization, aggregation, deduplication, and sorting within the Data Manipulation (SQL/Python) domain, focusing on practical query-writing and data-processing skills.

  Data Manipulation (SQL/Python)Onsite
• MetaMedium·Data Scientist
  Analyze Hashtag Follow Behavior with SQL Queries

  This question evaluates a candidate's competency in data manipulation and analytical SQL techniques—specifically aggregation, filtering, joins, and calculation of proportions—to analyze hashtag follow behavior.

  Data Manipulation (SQL/Python)Onsite
• MetaMedium·Data Scientist
  Analyze Oculus App Engagement with SQL Queries

  This question evaluates proficiency in SQL-based data manipulation and aggregation, time-window filtering, percentage calculations, and the application of basic statistical hypothesis testing for comparing user engagement across categories.

  Data Manipulation (SQL/Python)Onsite
• MetaMedium·Data Scientist
  Analyze Thirty-Day Ad Performance with SQL

  This problem tests proficiency in window functions, time-based aggregation, and join logic to analyze multi-dimensional advertising performance metrics.

  Data Manipulation (SQL/Python)OnsiteSQLAggregationTime-Series
• MetaMedium·Data Scientist
  Calculate Ad Performance with Click-Through and Conversion Rates

  This question evaluates a candidate's competency in designing and computing ad-performance KPIs by performing data manipulation, joining event tables, applying time-window filters, and aggregating metrics using SQL or Python.

  Data Manipulation (SQL/Python)Onsite