Data Manipulation (SQL/Python) interview questions

Showing 421–446 of 446 results

• Compute ETA shift and conversion upliftMedium

  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.

  UberData ScientistData Manipulation (SQL/Python)Technical Screen
• Write SQL and Pandas for Uber TripsMedium

  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.

  UberData ScientistData Manipulation (SQL/Python)Technical Screen
• Write SQL/Python for CTR analyticsMedium

  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.

  UberData ScientistData Manipulation (SQL/Python)Technical Screen
• Calculate Average Event Value by User IDMedium

  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.

  UpstartData ScientistData Manipulation (SQL/Python)Technical Screen
• Calculate User Revenue and Session Duration in PythonMedium

  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.

  UpstartData ScientistData Manipulation (SQL/Python)Technical Screen
• Query the Top Ten Customers by Recent SpendMedium

  This problem tests proficiency in SQL aggregation, window functions, and joining across normalized transactional tables to perform time-bound analysis.

  VisaSoftware Engineer IData Manipulation (SQL/Python)Online Assessment
• Analyze a Regression Dataset and Build a Safe File-Processing UtilityEasy

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

  VoleonData ScientistData Manipulation (SQL/Python)Technical Screen
• Analyze time-zoned events with pandasMedium

  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.

  VoleonData ScientistData Manipulation (SQL/Python)Technical Screen
• Load and visualize large CSV robustlyMedium

  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.

  VoleonData ScientistData Manipulation (SQL/Python)Technical Screen
• Pre-process Financial Data for Linear Regression ModelingMedium

  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.

  VoleonData ScientistData Manipulation (SQL/Python)Technical Screen
• Clean scores and return top 5 studentsEasy

  This problem tests your ability to clean tabular data by performing conditional imputation and applying window functions to rank performance metrics.

  WayfairData ScientistData Manipulation (SQL/Python)Online Assessment
• Find top buyer in earliest 10-year windowEasy

  This question tests the ability to filter data by date ranges, group transactional records by user, and use window functions or aggregations to identify a specific record based on criteria.

  WayfairData ScientistData Manipulation (SQL/Python)Online Assessment
• Solve SQL and Python OA tasksEasy

  This question evaluates SQL and pandas data-manipulation skills, including joined aggregations over date windows, handling of missing values with median imputation, and multi-criteria sorting and tie-breaking.

  WayfairData ScientistData Manipulation (SQL/Python)Online Assessment
• Analyze User Ride Activity with SQLMedium

  Solve four SQL analytics tasks on ride and user tables: inactive users, monthly rides and ratings, low-frequency users by city, and first-versus-third active-month ratings. The walkthrough emphasizes outer joins, date boundaries, aggregation grain, window functions, and weighted averages.

  WaymoData ScientistData Manipulation (SQL/Python)Online Assessment
• Compute Ride Metrics in SQLMedium

  This question evaluates SQL data manipulation skills, covering joins between normalized tables, timezone-aware date filtering and calendar-month grouping, aggregation and NULL-aware averages, counting distinct and inactive users, and cohort/retention analysis.

  WaymoData ScientistData Manipulation (SQL/Python)Online Assessment
• Implement a Safe Average Function in PythonMedium

  Implement a Python average function that returns zero for an empty list and explain its time and space complexity. Clarify numeric types, missing values, NaN behavior, streaming alternatives, precision concerns, and the tests needed for a reliable data utility.

  WaymoData ScientistData Manipulation (SQL/Python)Online Assessment
• Merge overlapping intervals per group in pandasEasy

  This problem tests your ability to handle temporal data by merging overlapping time intervals within distinct groups using pandas and custom logic.

  WaymoData ScientistData Manipulation (SQL/Python)Onsite
• SQL: Average Ride Rating in Users' First and Third Active Months by CityMedium

  Write a PostgreSQL query that compares average ride ratings in each user's first and third active months, grouped by city and averaged over individual rides. Tests ranking distinct active months per user, window functions, NULL ratings and weighting.

  WaymoData ScientistData Manipulation (SQL/Python)Onsite
• SQL: Users With No Rides in the Most Recent 7-Day Window, Including Non-RidersMedium

  Write a PostgreSQL query that lists users with no rides in the most recent 7-day window, including users who never rode. Tests anchoring a date window to the data, inclusive boundaries, and anti-join logic that keeps users without matching rides.

  WaymoData ScientistData Manipulation (SQL/Python)Onsite
• Top 5 Most Efficient Vehicle ModelsMedium

  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.

  WaymoData AnalystData Manipulation (SQL/Python)Technical Screen
• Compute window averages and merge intervalsMedium

  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.

  WeRideData ScientistData Manipulation (SQL/Python)Technical Screen
• Write SQL for rolling default ratesMedium

  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.

  Wells FargoData ScientistData Manipulation (SQL/Python)Technical Screen
• Calculate Monthly Customer RetentionMedium

  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.

  WhatnotData ScientistData Manipulation (SQL/Python)Technical Screen
• Compute a Seven-Day Rolling AverageMedium

  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.

  WhatnotData ScientistData Manipulation (SQL/Python)Technical Screen
• Calculate Total Spend and Identify Key User MetricsMedium

  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.

  YahooData ScientistData Manipulation (SQL/Python)Technical Screen
• Explain Joins and Write Coupon SQLMedium

  This problem tests your ability to perform multi-table joins, handle temporal data, and apply conditional aggregation to compute business KPIs like conversion rates and total transaction value.

  eBayData AnalystData Manipulation (SQL/Python)HR Screen