Data Manipulation (SQL/Python) Interview Questions

Showing 1–30 of 256 results

• AdobeMedium·Software Engineer
  Implement scalable word count locally

  This question evaluates understanding of scalable word-count processing, including tokenization and normalization rules, streaming or external-memory techniques for inputs larger than RAM, and efficient top‑K frequency computation.

  Data Manipulation (SQL/Python)Technical Screen
• AAeoneaMedium·Software Engineer
  Fill missing values with means

  This question evaluates proficiency in missing-data imputation and pandas DataFrame manipulation, testing skills in column-wise aggregation, group-wise operations, and edge-case handling within the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Technical Screen
• AffirmMedium·Data Scientist
  Analyze Loan Payments Using Pandas for Key Insights

  This question evaluates data manipulation and aggregation skills using pandas, focusing on computing per-loan payment totals, payment success rates, and a repayment-status flag. It is commonly asked to assess applied data wrangling and numeric summarization abilities in the Data Manipulation (SQL/Python) domain, testing practical application rather than purely conceptual understanding.

  Data Manipulation (SQL/Python)Technical Screen
• AffirmMedium·Software Engineer
  Analyze Returning Borrowers Across Two Days of Logs

  Process two daily loan activity logs to identify users active on both days with diverse loan types, then score them using a configurable trust policy.

  Data Manipulation (SQL/Python)Technical ScreenData ManipulationHash TablePython
• AgodaEasy·Data Engineer
  Find the Longest Calendar-Day Streak with All Employees Present

  Identify dates when every employee is present and compute the longest consecutive streak, treating missing attendance as a gap.

  Data Manipulation (SQL/Python)Technical Screen
• AgodaEasy·Data Engineer
  Rank Customers by Monthly Revenue with Competition Ties

  Aggregate customer revenue by month and return top competition ranks, preserving tied totals and deterministic display order.

  Data Manipulation (SQL/Python)Technical Screen
• AgodaEasy·Data Engineer
  Return Every Employee with a Mapped or Default Unique ID

  Join employees to unique-ID mappings while preserving unmatched employees and applying an explicit fallback identifier.

  Data Manipulation (SQL/Python)Technical Screen
• AirbnbMedium·Data Scientist
  Aggregate User Activity, Fit Regression, Interpret Coefficients

  This question evaluates skills in relational data aggregation and feature engineering using SQL alongside fitting and interpreting regression models in Python, targeting competencies in transforming event-level user metrics into a modeling dataset and interpreting coefficient estimates.

  Data Manipulation (SQL/Python)Technical Screen
• AirbnbMedium·Data Scientist
  Build panel in SQL; run causal regression

  This question evaluates proficiency in SQL-based panel data construction, time-series aggregation and rolling-window metrics together with applied causal inference in Python, including two-way fixed-effects difference-in-differences, clustered standard errors, lead tests, and count-model re-specification; it targets Data Manipulation (SQL/Python) skills relevant for Data Scientist roles. It is commonly asked because it combines practical implementation and conceptual understanding—testing temporal joins, treatment timing, avoidance of lookahead/immortal-time biases and late-adopter issues, interpretation of interaction coefficients and confidence intervals, and the ability to reason about identification and model choice.

  Data Manipulation (SQL/Python)Technical Screen
• AirbnbMedium·Data Scientist
  Compute C/T metrics from bookings and visits

  This question evaluates time-window attribution, visit deduplication, control vs treatment metric computation, aggregation of conversions and revenue, and statistical interval estimation (Wilson CIs), testing practical data manipulation skills with SQL or Python in the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Technical Screen
• AirbnbMedium·Data Scientist
  Compute browsing metrics in Python from logs

  This question evaluates a candidate's ability to perform event-level data manipulation and analytics in Python and SQL, covering sessionization, time-zone–consistent timestamp handling, deduplication, funnel construction, segment-level aggregation, conversion-rate computation, and statistical confidence intervals such as the Wilson CI.

  Data Manipulation (SQL/Python)Technical Screen
• AirbnbMedium·Software Engineer
  Review a geospatial Python module

  This question evaluates proficiency in geospatial data processing and Python code review, including detection of correctness bugs, numerical stability issues, CRS handling, performance optimization, refactoring, testing strategy, and security considerations when working with CSV/GeoJSON datasets.

  Data Manipulation (SQL/Python)Technical Screen
• AmazonMedium·Data Scientist
  Append country tables and rank salaries in USD

  This question evaluates skills in data manipulation and integration, specifically appending country-level tables, normalizing salaries via historical exchange rates, deduplicating by a composite primary key, and ranking results, with implementations expected in both SQL and pandas.

  Data Manipulation (SQL/Python)Technical Screen
• AmazonMedium·Data Scientist
  Build DiD dataset with SQL

  This question evaluates proficiency in SQL-based data manipulation and panel construction for staggered-adoption difference-in-differences, testing skills in handling adoption dates, event-time calculations, boundary conditions, and aggregation in the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Technical Screen
• AmazonMedium·Data Scientist
  Calculate Defect Rate and Identify Top Lanes for Carriers

  This question evaluates competency in SQL-based data manipulation for a data scientist role, covering joins, aggregations, date-based filtering, metric computation, and use of window functions to derive week-over-week changes.

  Data Manipulation (SQL/Python)Technical Screen
• AmazonMedium·Data Scientist
  Calculate Monthly Revenue from Orders in 2023

  This question evaluates proficiency in SQL-based data manipulation, including aggregation, date extraction, and combining transactional tables to produce business metrics, and falls under the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Technical Screen
• AmazonMedium·Data Scientist
  Calculate Weekly, Monthly Hours Watched by Premium Users

  This question evaluates a candidate's ability to perform time-based aggregation and data manipulation in SQL and Python, including grouping, filtering by subscription status, and converting minute-level metrics into hours.

  Data Manipulation (SQL/Python)Technical Screen
• AmazonMedium·Data Scientist
  Compute a Rolling Seven-Day Revenue Sum with Missing Dates

  Tests the ability to write a PostgreSQL query that generates a continuous daily time series per seller, handles missing sales dates, filters by order status, and computes a rolling 7-day revenue sum.

  Data Manipulation (SQL/Python)Technical Screensqlpostgresqlwindow-functions
• AmazonMedium·Software Engineer
  Compute unique visitors per department from clicks

  This question evaluates a candidate's skill in data manipulation and deduplication using SQL/Python, specifically mapping hierarchical product metadata to departments and computing unique user counts without double-counting, and is categorized under Data Manipulation (SQL/Python).

  Data Manipulation (SQL/Python)Technical Screen
• AmazonMedium·Data Scientist
  Consolidate and Rank Global Salaries in USD

  This question evaluates data consolidation, currency normalization, and ranking competencies, measuring the ability to combine country-specific employee data and convert salaries into a common USD basis within the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Technical Screen
• AmazonMedium·Data Scientist
  Create Country-Level Spend Report Using Pandas

  This question evaluates proficiency in data manipulation using pandas, including relational joins and aggregation to produce country-level summary statistics like totals and averages.

  Data Manipulation (SQL/Python)Technical Screen
• AmazonMedium·Data Scientist
  Create SQL Queries for Sales and Customer Analysis

  This question evaluates proficiency in SQL-based data manipulation, covering aggregation and dispersion metrics, null handling, relational joins, and window functions for ranking customers.

  Data Manipulation (SQL/Python)Technical Screen
• AmazonMedium·Data Scientist
  Design Incremental Load Process for Large Relational Table

  This question evaluates understanding of incremental loading, change-data-capture, idempotent upsert logic, deduplication, partitioning, and data quality controls for large relational tables within the Data Manipulation (SQL/Python) domain.

  Data Manipulation (SQL/Python)Technical Screen
• AmazonMedium·Data Scientist
  Explore Subscription Patterns and Status Transitions with SQL/Pandas

  This question evaluates proficiency in time-series and event-sequence data manipulation, temporal aggregation, and data quality validation using SQL and pandas, focusing on identifying status transitions and date-based uniqueness.

  Data Manipulation (SQL/Python)Technical Screen
• AmazonMedium·Data Scientist
  Find recommended friend pairs by shared songs

  This question evaluates data manipulation skills—specifically SQL joins and anti-joins, deduplication, date/time handling, aggregation, and pairwise overlap computation—within the Data Manipulation (SQL/Python) domain at a practical implementation level requiring query-writing to produce per-day overlap counts.

  Data Manipulation (SQL/Python)Technical Screen
• AmazonMedium·Data Scientist
  List Top Customers and Monthly Order Counts in SQL

  This question evaluates a candidate's ability to perform SQL aggregation, grouping, top‑N selection, and date-based summarization to compute customer spend and chronological monthly order counts.

  Data Manipulation (SQL/Python)Technical Screen
• AmazonMedium·Software Engineer
  Manipulate time-series with Pandas groupby

  This question evaluates proficiency in time-series data manipulation, including timezone-aware datetime parsing and DST handling, groupby and rolling-window aggregations, resampling to fill calendar gaps, and user-level retention and DAU calculations using idiomatic, vectorized Pandas or SQL operations.

  Data Manipulation (SQL/Python)Technical Screen
• AmazonEasy·Business Intelligence Engineer
  Reason About Composite Join Keys and Predicate Placement

  This question tests your understanding of composite join keys, the critical semantic difference between filtering in the `ON` versus `WHERE` clause during joins, and techniques to prevent row fan-out.

  Data Manipulation (SQL/Python)Technical Screensqljoinsdata-manipulation
• AmazonMedium·Data Scientist
  Select Top Customers Using Transaction Data Filters

  This question evaluates a candidate's ability to perform data aggregation, filtering, ranking, and deterministic tie-breaking on transactional datasets, assessing skills in Data Manipulation (SQL/Python).

  Data Manipulation (SQL/Python)Technical Screen
• AmazonMedium·Data Scientist
  Understand SQL: DELETE vs TRUNCATE, VIEW vs TABLE, CROSS JOIN

  This question evaluates a candidate's understanding of core SQL concepts and data-manipulation competencies, covering the behavior of data-deletion operations, distinctions between logical and physical data structures, and join semantics.

  Data Manipulation (SQL/Python)Technical Screen