Data Manipulation (SQL/Python) Interview Questions

Showing 151–180 of 446 results

• 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
• 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
• 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
• 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
  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 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
• 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
• GustoMedium·Data Scientist
  Calculate Net Pay Change for Q1 2023 Decreases

  This question evaluates a candidate's data-manipulation competency in SQL and Python, focusing on computing net pay, aggregating over a calendar quarter, and measuring percentage change between recent pay periods.

  Data Manipulation (SQL/Python)Technical Screen
• 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
• IBMMedium·Software Engineer
  Find Customers Whose Average Website Resource Usage Exceeds 50 Percent

  Practice PostgreSQL aggregation with HAVING, customer-level average resource utilization, strict thresholds, two-decimal rounding, and ascending email output.

  Data Manipulation (SQL/Python)Online Assessment
• IBMEasy·Data Scientist
  Write SQL to compute max and min marks

  This question evaluates a candidate's ability to perform basic data aggregation in SQL, specifically computing summary statistics from a single table. It is commonly asked to assess competence in data manipulation and query result shaping within the Data Manipulation (SQL/Python) domain, focusing on practical application of SQL aggregation rather than purely conceptual theory.

  Data Manipulation (SQL/Python)Online Assessment
• InstacartMedium·Data Scientist
  Aggregate weekly revenue and attribute 4% drop

  This question evaluates a candidate's ability to perform time-based revenue aggregation, filtering, percent-change calculation, and geo-level attribution using SQL or Python, with attention to ISO-week alignment and revenue computation.

  Data Manipulation (SQL/Python)Technical Screen
• InstacartMedium·Data Scientist
  Calculate Weekly Revenue and Order Count for Standard Deliveries

  This question evaluates proficiency in time-based aggregations and filtering on order-level transactional data to compute weekly revenue and order counts specifically for standard delivery orders.

  Data Manipulation (SQL/Python)Technical Screen
• InstacartMedium·Data Scientist
  Explain handling very large datasets

  This question evaluates a candidate's competence in ingesting and processing very large datasets, covering storage formats and partitioning, memory and compute constraints, schema evolution, data quality checks, indexing strategies, tool selection in SQL/Python ecosystems, and code-level performance optimizations.

  Data Manipulation (SQL/Python)HR Screen
• 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
• InstacartMedium·Data Scientist
  Write SQL to rank advertisers and profitability

  This question evaluates a candidate's ability to perform ANSI SQL data transformations including unpivoting denormalized tables, aggregations, joins, and financial metric calculations such as net profit and profit margin while handling nulls and tie-breaking rules.

  Data Manipulation (SQL/Python)Technical Screen
• IntuitMedium·Data Scientist
  Compute churn and revenue churn in SQL

  This question evaluates a candidate's ability to compute subscription churn and revenue retention metrics from monthly snapshots, exercising SQL data-manipulation competencies such as handling missing or null MRR values, deduplicating snapshot loads, joining across months, and aggregating MRR changes.

  Data Manipulation (SQL/Python)HR Screen
• IntuitMedium·Data Scientist
  Compute monthly new subscribers and YoY deltas

  This question evaluates proficiency in SQL time-series aggregation and cohort identification, including generating a month spine, computing year-over-year deltas, handling nulls and division-by-zero, and identifying partial months via a data watermark.

  Data Manipulation (SQL/Python)Technical Screen
• IntuitMedium·Data Scientist
  Design an idempotent churn ETL pipeline

  This question evaluates a candidate's competence in designing robust ETL pipelines, covering idempotent processing, handling late-arriving streaming subscription events, SCD Type 2 slowly changing dimensions, incremental backfills, data quality checks, upsert/merge semantics, and metric versioning.

  Data Manipulation (SQL/Python)HR Screen
• IntuitMedium·Data Scientist
  Exclude free subscribers via anti-join

  This question evaluates competency with SQL anti-joins, join semantics, deduplication, and correct pre-aggregation filtering to prevent excluded entities from affecting aggregated monthly and year-over-year metrics; it is categorized as Data Manipulation (SQL/Python) for a Data Scientist role.

  Data Manipulation (SQL/Python)Technical Screen
• IntuitMedium·Machine Learning Engineer
  Longest Meeting-Free Business-Hours Block per Employee and Day

  Find each employee’s longest meeting-free business-hours interval using clipped attendance, merged overlaps, complete day coverage, and SQL window logic.

  Data Manipulation (SQL/Python)Technical Screen
• IntuitEasy·Data Scientist
  Pivot daily users and revenue by platform

  This question evaluates data manipulation and aggregation skills, specifically computing distinct user counts and revenue aggregates and reshaping results by platform; it is in the Data Manipulation (SQL/Python) domain and assesses practical application rather than purely conceptual understanding.

  Data Manipulation (SQL/Python)Technical Screen