Data Manipulation (SQL/Python) interview questions

Showing 1–30 of 446 results

• Find Top-Funded Villages and Their Top ProjectsMedium

  Tests the ability to write a multi-level SQL aggregation with window functions, exact date filtering, tie handling, and deterministic sorting.

  AdobeSoftware EngineerData Manipulation (SQL/Python)Online Assessment
• Implement scalable word count locallyMedium

  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.

  AdobeSoftware EngineerData Manipulation (SQL/Python)Technical Screen
• Solve SQL and Python Data Manipulation TasksMedium

  This question evaluates proficiency in SQL event-log aggregation and basic Python data-structure transformations, assessing competencies such as counting distinct users, identifying frequent event types, and performing dictionary/list/string manipulations.

  AdobeData ScientistData Manipulation (SQL/Python)Onsite
• Fill missing values with meansMedium

  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.

  AeoneaSoftware EngineerData Manipulation (SQL/Python)Technical Screen
• Analyze Loan Payments Using Pandas for Key InsightsMedium

  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.

  AffirmData ScientistData Manipulation (SQL/Python)Technical Screen
• Analyze Returning Borrowers Across Two Days of LogsMedium

  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.

  AffirmSoftware EngineerData Manipulation (SQL/Python)Technical Screen
• Implement and debug event filtering in PythonMedium

  This question evaluates proficiency in data manipulation and algorithmic reasoning in Python, covering event filtering, stable sorting, deduplication by identifier, timestamp windowing, and analysis of time and space complexity.

  AffirmSoftware EngineerData Manipulation (SQL/Python)Onsite
• Find the Longest Calendar-Day Streak with All Employees PresentEasy

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

  AgodaData EngineerData Manipulation (SQL/Python)Technical Screen
• Rank Customers by Monthly Revenue with Competition TiesEasy

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

  AgodaData EngineerData Manipulation (SQL/Python)Technical Screen
• Return Every Employee with a Mapped or Default Unique IDEasy

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

  AgodaData EngineerData Manipulation (SQL/Python)Technical Screen
• Aggregate User Activity, Fit Regression, Interpret CoefficientsMedium

  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.

  AirbnbData ScientistData Manipulation (SQL/Python)Technical Screen
• Build panel in SQL; run causal regressionMedium

  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.

  AirbnbData ScientistData Manipulation (SQL/Python)Technical Screen
• Calculate the Median House Price in Each CityMedium

  Tests your ability to compute grouped medians in PostgreSQL, properly handling even and odd row counts, duplicate price values, and numeric precision.

  AirbnbData EngineerData Manipulation (SQL/Python)Onsite
• Compute C/T metrics from bookings and visitsMedium

  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.

  AirbnbData ScientistData Manipulation (SQL/Python)Technical Screen
• Compute browsing metrics in Python from logsMedium

  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.

  AirbnbData ScientistData Manipulation (SQL/Python)Technical Screen
• Describe How You Use SQL in Data Science WorkMedium

  This question assesses a candidate's ability to articulate their technical workflow in SQL, focusing on data extraction strategies, quality assurance, and how technical analysis translates into business impact.

  AirbnbData ScientistData Manipulation (SQL/Python)HR Screen
• Estimate Page-Visit Duration from the Next User EventMedium

  Implement logic to compute user page-visit durations based on adjacent event timestamps, deterministically resolving ties and handling boundary conditions for terminal events.

  AirbnbData EngineerData Manipulation (SQL/Python)Onsite
• Find Hosts Meeting Listing, Rating, and Review-Coverage RulesMedium

  Tests the ability to write a SQL query performing multi-table aggregations, date-windowed conditional logic, and host-level qualification filtering across listing counts, average ratings, and recent review coverage.

  AirbnbData EngineerData Manipulation (SQL/Python)Onsite
• Find Users with Seven Consecutive Impression DaysMedium

  Tests your ability to identify consecutive calendar-day activity streaks in PostgreSQL using window functions, date arithmetic, and deduplication.

  AirbnbData EngineerData Manipulation (SQL/Python)Onsite
• Review a geospatial Python moduleMedium

  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.

  AirbnbSoftware EngineerData Manipulation (SQL/Python)Technical Screen
• Compute Moderation View MetricsMedium

  An Airwallex Data Scientist onsite SQL question on content-moderation analytics. You write three queries over a per-day view table and a one-to-many post/violation-type policy-score table: distinct posts with 10+ views in the last 7 days, Nudity view prevalence over the last 30 days, and a monthly violation-type distribution. The core challenge is windowed aggregation plus avoiding view double-counting across violation types.

  AirwallexData ScientistData Manipulation (SQL/Python)Onsite
• Answer SQL MCQs with reasoningMedium

  This question evaluates proficiency in SQL query semantics and reasoning about relational operations—specifically INNER, LEFT, and RIGHT JOIN behavior; GROUP BY and HAVING usage; NULL handling in comparisons and aggregates; window functions such as ROW_NUMBER and RANK; and the logical query processing order—within the Data Manipulation (SQL/Python) domain. It is commonly asked to assess the ability to interpret and justify query outcomes versus alternative answers, testing both conceptual understanding of relational theory and practical application of SQL query behavior.

  Akuna CapitalSoftware EngineerData Manipulation (SQL/Python)Online Assessment
• Evaluate SQL expressions for zeroMedium

  This question evaluates understanding of SQL type coercion, boolean semantics, comparison operators, and dialect-specific evaluation behavior (for example, MySQL), focusing on interpreting expressions that may evaluate to zero.

  Akuna CapitalSoftware EngineerData Manipulation (SQL/Python)Online Assessment
• Analyze Seller Compliance and Customer Purchase PatternsMedium

  This question evaluates a candidate's ability to manipulate and analyze temporal and categorical datasets, covering competencies such as event sequencing, set-based filtering, and cross-table aggregation in SQL/Python.

  AmazonBusiness Intelligence EngineerData Manipulation (SQL/Python)Onsite
• Analyze Top 10 Items' Revenue Contribution by CategoryMedium

  This question evaluates proficiency in data manipulation and analytical querying—aggregation, window functions, ranking and revenue attribution—alongside conceptual knowledge of normalization versus denormalization and ETL pipeline design.

  AmazonData ScientistData Manipulation (SQL/Python)Onsite
• Append country tables and rank salaries in USDMedium

  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.

  AmazonData ScientistData Manipulation (SQL/Python)Technical Screen
• Build DID panel and compute effects in SQLMedium

  This question evaluates proficiency in panel data construction and causal inference workflows, specifically difference-in-differences estimation, SQL aggregation for user-week metrics, and propensity-score matching implemented in Python.

  AmazonData ScientistData Manipulation (SQL/Python)HR Screen
• Build DiD dataset with SQLMedium

  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.

  AmazonData ScientistData Manipulation (SQL/Python)Technical Screen
• Calculate Defect Rate and Identify Top Lanes for CarriersMedium

  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.

  AmazonData ScientistData Manipulation (SQL/Python)Technical Screen
• Calculate Monthly Revenue from Orders in 2023Medium

  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.

  AmazonData ScientistData Manipulation (SQL/Python)Technical Screen