Data Manipulation (SQL/Python) interview questions

Showing 121–150 of 446 results

• Process CSV for portfolio returns and metricsMedium

  This question evaluates a candidate's competency in time-series data manipulation, financial return computation, portfolio analytics, numerical stability, modular software design (clear function boundaries and unit tests), and algorithmic complexity analysis within the Data Manipulation (SQL/Python) domain.

  DRWMachine Learning EngineerData Manipulation (SQL/Python)Online Assessment
• Find Top-5 Similar RowsHard

  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.

  DatabricksData ScientistData Manipulation (SQL/Python)Technical Screen
• Find top-5 most similar rows across datasetsEasy

  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.

  DatabricksData ScientistData Manipulation (SQL/Python)Technical Screen
• Parse JSON Accounts into Typed Python Objects and Prepare FeaturesEasy

  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.

  DaveMachine Learning EngineerData Manipulation (SQL/Python)Onsite
• Write complex SQL for streaming funnelsMedium

  This question evaluates a candidate's ability to write complex ANSI-SQL for event-level funnel analysis, including temporal windowing, user attribution, multi-stage aggregation, and handling of event schemas.

  DisneyData ScientistData Manipulation (SQL/Python)HR Screen
• Analyze Driver Requests for Food Delivery OrdersMedium

  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.

  DoorDashData ScientistData Manipulation (SQL/Python)Technical Screen
• Calculate Late Delivery Percentage and Top CustomersMedium

  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.

  DoorDashData ScientistData Manipulation (SQL/Python)Technical Screen
• Calculate Monthly Restaurant Sales GrowthEasy

  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.

  DoorDashData ScientistData Manipulation (SQL/Python)Technical Screen
• Calculate Order Request MetricsHard

  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.

  DoorDashData ScientistData Manipulation (SQL/Python)Onsite
• Compute courier pay with peak-hour rulesMedium

  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.

  DoorDashSoftware EngineerData Manipulation (SQL/Python)Technical Screen
• Compute dasher pay from deliveriesMedium

  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.

  DoorDashSoftware EngineerData Manipulation (SQL/Python)Onsite
• Compute dasher payout from API dataMedium

  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.

  DoorDashSoftware EngineerData Manipulation (SQL/Python)Onsite
• Compute rolling cold-delivery rates with windowsMedium

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

  DoorDashData ScientistData Manipulation (SQL/Python)Onsite
• Find Monthly Top Customers Excluding High-Frequency UsersEasy

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

  DoorDashData ScientistData Manipulation (SQL/Python)Technical Screen
• Generate Weekly Revenue and Engagement Summary with PandasMedium

  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.

  DoorDashData ScientistData Manipulation (SQL/Python)Technical Screen
• Model schema and query new-market readinessMedium

  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.

  DoorDashData ScientistData Manipulation (SQL/Python)Onsite
• Solve multi-part SQL with sliding windowsMedium

  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.

  DoorDashData ScientistData Manipulation (SQL/Python)Technical Screen
• Write SQL for deliveries analyticsMedium

  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.

  DoorDashData ScientistData Manipulation (SQL/Python)Technical Screen
• Write SQL for percent and window changesMedium

  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.

  DoorDashData ScientistData Manipulation (SQL/Python)Technical Screen
• Write SQL to backtest refund policyMedium

  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.

  DoorDashData ScientistData Manipulation (SQL/Python)Technical Screen
• Write complex SQL on DoorDash dataMedium

  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.

  DoorDashData ScientistData Manipulation (SQL/Python)Onsite
• Architect cloud data ingestion patternsMedium

  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.

  EYData ScientistData Manipulation (SQL/Python)Technical Screen
• Design logical model and consumptionMedium

  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.

  EYData ScientistData Manipulation (SQL/Python)Technical Screen
• Map sources to functional dataset with SQLMedium

  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.

  EYData ScientistData Manipulation (SQL/Python)Technical Screen
• Audit and onboard unfamiliar datasets safelyMedium

  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.

  ExpediaData ScientistData Manipulation (SQL/Python)Onsite
• Compare Spark RDDs, DataFrames, and SQL Performance GainsMedium

  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.

  ExperianData ScientistData Manipulation (SQL/Python)Technical Screen
• Compare Spark RDDs, DataFrames, and Spark SQL BenefitsMedium

  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.

  ExperianData ScientistData Manipulation (SQL/Python)Technical Screen
• Assess SQL joins, unions, windows, dedup, and pandasMedium

  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.

  Fannie MaeData ScientistData Manipulation (SQL/Python)Technical Screen
• Understand SQL Aggregations and Joins: Key Differences ExplainedMedium

  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.

  Fannie MaeData ScientistData Manipulation (SQL/Python)Onsite
• Write SQL to verify quarterly sales declineMedium

  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.

  Fetch RewardsData ScientistData Manipulation (SQL/Python)Technical Screen