Data Manipulation (SQL/Python) Interview Questions

Showing 61–90 of 256 results

• CCloudTrucksMedium·Data Scientist
  Calculate Average Rate and Cumulative Jobs by State

  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.

  Data Manipulation (SQL/Python)Technical Screen
• CoupangMedium·Data Scientist
  Monthly Cohort Retention

  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.

  Data Manipulation (SQL/Python)Technical ScreenSQLRetentionCohort Analysis
• DatabricksHard·Data Scientist
  Find Top-5 Similar Rows

  This question tests the ability to implement a pairwise similarity comparison between rows using a custom distance metric while handling missing data and ranking results.

  Data Manipulation (SQL/Python)Technical ScreenPandasSimilarityData Manipulation
• DatabricksEasy·Data Scientist
  Find top-5 most similar rows across datasets

  This question evaluates understanding and implementation of similarity/distance metrics (e.g., MSE), data preprocessing issues such as handling NULLs and feature scaling, and the ability to perform efficient cross-dataset comparisons and top-k ranking.

  Data Manipulation (SQL/Python)Technical Screen
• DoorDashMedium·Data Scientist
  Analyze Driver Requests for Food Delivery Orders

  This question evaluates data manipulation and analytical SQL skills in the Data Manipulation (SQL/Python) domain, focusing on window functions for ranking and temporal comparison, joins, and aggregation when analyzing orders and driver requests.

  Data Manipulation (SQL/Python)Technical Screen
• DoorDashMedium·Data Scientist
  Calculate Late Delivery Percentage and Top Customers

  This question evaluates data manipulation and aggregation skills for a Data Scientist role, focusing on date handling, computing the percentage of late orders, and ranking customers using SQL or Python.

  Data Manipulation (SQL/Python)Technical Screen
• DoorDashEasy·Data Scientist
  Calculate Monthly Restaurant Sales Growth

  This problem tests the ability to aggregate transactional data by month and use window functions to calculate percentage growth between consecutive periods while handling null values.

  Data Manipulation (SQL/Python)Technical ScreenSQLWindow FunctionsAggregation
• DoorDashMedium·Software Engineer
  Compute courier pay with peak-hour rules

  This question evaluates a candidate's data manipulation skills and competency in implementing business rules, including robust error handling, aggregation for per-day totals, minimum-pay enforcement, and time-window (peak-hour) pay adjustments.

  Data Manipulation (SQL/Python)Technical Screen
• DoorDashEasy·Data Scientist
  Find Monthly Top Customers Excluding High-Frequency Users

  This problem tests proficiency in window functions, filtering based on aggregated metrics, and handling ranking logic in SQL.

  Data Manipulation (SQL/Python)Technical ScreenSQLWindow FunctionsData Aggregation
• DoorDashMedium·Data Scientist
  Generate Weekly Revenue and Engagement Summary with Pandas

  This question evaluates proficiency in data manipulation and analytics with Pandas and SQL, covering aggregations, distinct purchase counts, lambda-based user-tier classification, and platform-level conversion-rate computation.

  Data Manipulation (SQL/Python)Technical Screen
• DoorDashMedium·Data Scientist
  Solve multi-part SQL with sliding windows

  This question evaluates a candidate's practical SQL data-manipulation skills, including time-based rolling aggregates, distinct counts and deduplication, multi-table joins, revenue aggregation and ranking, and session/streak computations within the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Technical Screen
• DoorDashMedium·Data Scientist
  Write SQL for deliveries analytics

  This question evaluates SQL data manipulation and analytics skills—specifically aggregation, conditional counting, time-window filtering, joins, and handling of nulls and zero-count periods.

  Data Manipulation (SQL/Python)Technical Screen
• DoorDashMedium·Data Scientist
  Write SQL for percent and window changes

  This question evaluates proficiency with advanced SQL techniques—specifically the use of CTEs and multiple window functions, row-level versus aggregated conditionals, time-windowed aggregations, generation of unit-day series including zero-order days, and correct handling of missing exposure labels for percent and change calculations.

  Data Manipulation (SQL/Python)Technical Screen
• DoorDashMedium·Data Scientist
  Write SQL to backtest refund policy

  This question evaluates advanced SQL data-manipulation and analytics competency, including time-based lateness calculations, window functions (PERCENT_RANK and rolling/partitioned windows), CTEs and joins, aggregation, and monetary-field handling for a policy-simulation scenario.

  Data Manipulation (SQL/Python)Technical Screen
• EYMedium·Data Scientist
  Architect cloud data ingestion patterns

  This question evaluates a candidate's ability to design end-to-end cloud data ingestion and serving architectures, including streaming and batch patterns, CDC/event sourcing/micro-batch trade-offs, partitioning and compaction strategies, idempotency, schema evolution, PII tokenization, cost controls, and incident simulation for chaos testing.

  Data Manipulation (SQL/Python)Technical Screen
• EYMedium·Data Scientist
  Design logical model and consumption

  This question evaluates understanding of logical data modeling, ERD-level design with keys and cardinalities, slowly changing dimensions, normalization versus denormalization trade-offs, and strategies for exposing curated datasets and read paths for analytics and regulatory consumers, indicating competency in data modeling and data delivery.

  Data Manipulation (SQL/Python)Technical Screen
• EYMedium·Data Scientist
  Map sources to functional dataset with SQL

  This question evaluates competency in SQL-based data manipulation and data engineering concepts, including handling late-arriving data and deduplication to the latest ingested record, joining and filtering for enrichment and KYC status, and computing exposure metrics such as gross/net notional, limit utilization, and breach flags.

  Data Manipulation (SQL/Python)Technical Screen
• ExperianMedium·Data Scientist
  Compare Spark RDDs, DataFrames, and SQL Performance Gains

  This question evaluates understanding of Spark abstractions (RDDs, DataFrames, Spark SQL), lazy evaluation, memory management, query optimization, and developer ergonomics within the Data Manipulation (SQL/Python) domain of distributed data processing.

  Data Manipulation (SQL/Python)Technical Screen
• ExperianMedium·Data Scientist
  Compare Spark RDDs, DataFrames, and Spark SQL Benefits

  This question evaluates understanding of Apache Spark core abstractions (RDDs, DataFrames, Spark SQL), execution model concepts such as lazy evaluation and optimizer-driven execution, and operational competency with managed cloud cluster job submission and monitoring, categorized under Data Manipulation (SQL/Python) for a Data Scientist role.

  Data Manipulation (SQL/Python)Technical Screen
• FMFannie MaeMedium·Data Scientist
  Assess SQL joins, unions, windows, dedup, and pandas

  This question evaluates proficiency in SQL and pandas data-manipulation techniques, including aggregations with NULL semantics, various join types, set operations and deduplication, window functions, and reconciling normalized and denormalized sources.

  Data Manipulation (SQL/Python)Technical Screen
• Fetch RewardsMedium·Data Scientist
  Write SQL to verify quarterly sales decline

  This question evaluates proficiency in SQL aggregation, time-series period-over-period comparison, same-store cohort analysis, correct grain handling, and treatment of returns, testing data manipulation and analytical SQL skills in the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Technical Screen
• FHFlatiron HealthMedium·Data Scientist
  Compute churn metrics and rank top students

  This question evaluates proficiency in data cleaning, transformation, aggregation, and deterministic ranking across R (tidyverse) and SQL, focusing on handling missing values, date arithmetic, aggregation and rounding of metrics, and top-N selection with tie-breaking.

  Data Manipulation (SQL/Python)Technical Screen
• FHFlatiron HealthMedium·Data Scientist
  Write SQL window functions for streaks

  This question evaluates proficiency with SQL window functions, date arithmetic, gap-and-island (streak) detection, rolling aggregates, and event-order sequencing to derive DAU, revenue metrics, and inactivity gaps.

  Data Manipulation (SQL/Python)Technical Screen
• GleanEasy·Data Scientist
  Compute DAU and rolling MAU with zero days

  This question evaluates a data scientist's proficiency in time-series analytics and data aggregation, focusing on computing daily active users and a 30-day rolling MAU from user and login tables, and falls under the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Technical Screen
• GoogleMedium·Data Scientist
  Analyze User Flags and Review Outcomes for Moderation Prioritization

  This question evaluates a data scientist's proficiency in SQL-based data manipulation, including joins, distinct counting and grouping, aggregations, string concatenation for user identity, and NULL handling across event and review tables.

  Data Manipulation (SQL/Python)Technical Screen
• GoogleMedium·Data Scientist
  Calculate User Deviation from Team Average Messages

  This question evaluates proficiency with pandas group-wise computations, broadcasting of group aggregates, and DataFrame manipulation without renaming columns.

  Data Manipulation (SQL/Python)Technical Screen
• GoogleMedium·Data Scientist
  Deduplicate events and rank products with SQL

  This question evaluates proficiency in SQL data manipulation and analytical querying—specifically deduplication using idempotency keys, window functions/CTEs for ranking, distinct aggregation by date, timestamp tie-breaking, and awareness of indexing and scalability trade-offs in large tables; the domain is Data Manipulation (SQL/Python).

  Data Manipulation (SQL/Python)Technical Screen
• GoogleMedium·Data Scientist
  Design Scalable Database and Analyze E-commerce Data

  This question evaluates relational database design and data-manipulation competencies, including schema modeling and normalization, SQL-based association analysis for co-purchases, and vectorized data transformation using Python/Pandas.

  Data Manipulation (SQL/Python)Technical Screen
• GoogleMedium·Data Scientist
  Design a scalable video platform database

  This question evaluates relational database design and data engineering competencies—including schema modeling, many-to-many relationships, idempotent ingest, indexing and partitioning, OLTP versus analytics integration, GDPR-compliant deletion strategies, and query formulation—within the Data Manipulation (SQL/Python) domain for a Data Scientist role. It is commonly asked to assess both conceptual understanding and practical application of scalable data architectures, performance tuning, and compliance trade-offs, focusing on the ability to reason about schema choices, read/write optimization, and analytics integration without implementation details.

  Data Manipulation (SQL/Python)Technical Screen
• GoogleMedium·Data Scientist
  Write SQL/Python for messy event data

  This question evaluates SQL and pandas proficiency for event-level data manipulation, including time zone–aware local date conversion, session-based deduplication, cohort and conversion metric calculation, and basic experiment sanity checking via a sample-ratio-mismatch p-value.

  Data Manipulation (SQL/Python)Technical Screen