Data Manipulation (SQL/Python) Interview Questions

Showing 241–256 of 256 results

• TwitchMedium·Data Scientist
  Solve advanced SQL for streaming analytics

  This question evaluates advanced SQL competency for streaming analytics — covering time-series aggregation, monthly bucketing across year boundaries, joins between event tables, window functions, and case-insensitive substring filtering; the domain is Data Manipulation (SQL/Python) and the level is practical application of SQL techniques.

  Data Manipulation (SQL/Python)Technical Screen
• UberMedium·Data Scientist
  Compute ETA shift and conversion uplift

  This question evaluates proficiency in SQL and Python for time‑series data manipulation and experimental analysis, covering window functions (7‑day percentile_cont medians), date spines and timezone‑aware timestamp truncation, conversion-rate aggregation, difference‑in‑differences uplift estimation, and CUPED adjustment with cluster‑robust standard errors. It is commonly asked to assess practical data engineering and applied statistics skills in the Data Manipulation (SQL/Python) domain, testing applied implementation and causal inference ability rather than only conceptual understanding.

  Data Manipulation (SQL/Python)Technical Screen
• UberMedium·Data Scientist
  Write SQL and Pandas for Uber Trips

  This question evaluates SQL and Pandas data manipulation competencies, including joins, time-based filtering, handling NULLs, aggregations, rate and ratio calculations, and windowed comparisons over grouped city-day data.

  Data Manipulation (SQL/Python)Technical Screen
• UberMedium·Data Scientist
  Write SQL/Python for CTR analytics

  This question evaluates proficiency in data manipulation and analytics, covering SQL windowing and deduplication for time‑windowed cohort metrics (48‑hour unique CTR) as well as practical Python model evaluation tasks such as precision‑recall plotting, weighted F1 threshold selection, and precision@top‑k.

  Data Manipulation (SQL/Python)Technical Screen
• UpstartMedium·Data Scientist
  Calculate Average Event Value by User ID

  This question evaluates a candidate's skill in group-wise aggregation and data transformation on JSON-like event streams, specifically computing per-user average metrics and producing ranked results.

  Data Manipulation (SQL/Python)Technical Screen
• UpstartMedium·Data Scientist
  Calculate User Revenue and Session Duration in Python

  This question evaluates proficiency in data manipulation and time-series event processing in Python, focusing on per-user revenue aggregation and sessionization to compute average session duration.

  Data Manipulation (SQL/Python)Technical Screen
• VoleonEasy·Data Scientist
  Analyze a Regression Dataset and Build a Safe File-Processing Utility

  Implement an atomic, non-destructive file-cleaning utility for tabular logs and a greedy collinearity filter to screen regression features based on target correlation.

  Data Manipulation (SQL/Python)Technical ScreenPythonPandasFeature Engineering
• VoleonMedium·Data Scientist
  Analyze time-zoned events with pandas

  This question evaluates proficiency in data manipulation and analysis with pandas and SQL, covering timezone-aware timestamp handling, event deduplication, cohort and retention metrics, currency parsing and FX conversion, revenue imputation, ARPU calculation, and scalability for high-volume event data.

  Data Manipulation (SQL/Python)Technical Screen
• VoleonMedium·Data Scientist
  Load and visualize large CSV robustly

  This question evaluates competency in large-scale data manipulation and preprocessing, including robust file I/O (delimiter and encoding detection), memory-efficient chunked loading, dtype enforcement, deduplication, imputation strategy design, outlier handling, and statistical visualization techniques.

  Data Manipulation (SQL/Python)Technical Screen
• VoleonMedium·Data Scientist
  Pre-process Financial Data for Linear Regression Modeling

  This question evaluates data preprocessing and feature engineering skills—handling missing values, winsorizing outliers, standardizing predictors, and assembling X/y datasets using Python/Pandas within the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Technical Screen
• WaymoMedium·Data Analyst
  Top 5 Most Efficient Vehicle Models

  This problem tests your ability to aggregate data from multiple tables, calculate performance metrics, and handle common SQL pitfalls like division by zero and NULLs.

  Data Manipulation (SQL/Python)Technical ScreenSQLAggregationJoins
• WeRideMedium·Data Scientist
  Compute window averages and merge intervals

  Compute window averages and merge intervals 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)Technical Screen
• Wells FargoMedium·Data Scientist
  Write SQL for rolling default rates

  This question evaluates proficiency with SQL window functions and time-series aggregation, specifically the ability to compute rolling metrics across customer segments by aggregating defaults and accounts over a moving 12-month window.

  Data Manipulation (SQL/Python)Technical Screen
• WhatnotMedium·Data Scientist
  Calculate Monthly Customer Retention

  A PostgreSQL interview problem about calculating monthly customer retention from activity data. It tests month-level cohort construction, distinct-user counting, correct denominator selection, and handling customers who return across calendar boundaries.

  Data Manipulation (SQL/Python)Technical Screen
• WhatnotMedium·Data Scientist
  Compute a Seven-Day Rolling Average

  A PostgreSQL interview problem about computing a seven-day rolling average from dated observations. Candidates must use window frames carefully, preserve the requested grain, handle incomplete history, and return deterministically ordered results.

  Data Manipulation (SQL/Python)Technical Screen
• YahooMedium·Data Scientist
  Calculate Total Spend and Identify Key User Metrics

  This question evaluates a data scientist's proficiency in transactional data manipulation using SQL and Python, focusing on aggregation, ranking, joins, date arithmetic, and numeric rounding to produce user-level and time-series metrics.

  Data Manipulation (SQL/Python)Technical Screen