Data Manipulation (SQL/Python) interview questions

Showing 361–390 of 446 results

• Many-to-Many Schema Design With a Join Table, Composite Primary Key, and QueriesMedium

  Design a relational schema modeling a many-to-many relationship between documents and tags using a join table with a composite primary key, index both lookup directions, and write queries handling outer joins and relational division.

  ScribdSoftware EngineerData Manipulation (SQL/Python)Onsite
• Most Popular Actors Query When Object IDs Mix Movies and People With Multiple RolesMedium

  A data query question about ranking the most popular actors when an object ID can refer to either a movie or a person, and one person can hold several roles in the same movie. It tests clarifying the popularity metric, filtering to actor credits, deduplicating person and movie pairs, checking object types, and defining a tie policy.

  ScribdSoftware EngineerData Manipulation (SQL/Python)Onsite
• Analyze Pirate Theme Usage Growth Over TimeMedium

  This question evaluates a candidate's ability to perform SQL-based data manipulation and time-series analysis, emphasizing joins, aggregation, time-bucketing, and definition of growth metrics to measure feature adoption.

  ShopifyData ScientistData Manipulation (SQL/Python)Technical Screen
• Analyze Pirated Theme UsageMedium

  Calculate monthly metrics on unlicensed theme usage and lost revenue by evaluating temporal interval overlaps between installation lifespans and calendar months.

  ShopifyData ScientistData Manipulation (SQL/Python)Technical Screen
• Analyze Pirated Theme Usage ImpactMedium

  This question evaluates a candidate's ability to perform data manipulation and analytics with SQL and Python to detect license compliance, construct month-level time windows, and quantify revenue impact from pirated theme usage.

  ShopifyData ScientistData Manipulation (SQL/Python)Technical Screen
• Calculate Week-over-Week Revenue Change Using SQLMedium

  This question evaluates proficiency in SQL time-series aggregation, weekly grouping, and prior-period comparison for calculating percentage changes in revenue.

  ShopifyData ScientistData Manipulation (SQL/Python)Technical Screen
• Determine Growth of Pirated Theme Installations Over YearsMedium

  This question evaluates proficiency in SQL-based data manipulation and analytics, focusing on computing time-based cohort percentages and conducting year-over-year trend analysis of merchant behavior.

  ShopifyData ScientistData Manipulation (SQL/Python)Onsite
• Measure Pirate-Theme Usage and Revenue Growth Over TimeMedium

  This question evaluates the ability to design time-based usage metrics and compute monthly revenue through data manipulation using SQL or Python, focusing on aggregating event and transaction records by calendar month.

  ShopifyData ScientistData Manipulation (SQL/Python)Technical Screen
• Calculate Average Camera Switches per SessionMedium

  Tests the ability to write a PostgreSQL query using window functions (LAG) to compute the average number of specific state transitions per session while accounting for zero-transition sessions and avoiding integer division.

  SnapchatData ScientistData Manipulation (SQL/Python)Technical Screen
• Compute CTR and metrics with pandasMedium

  This question evaluates proficiency in time-series and event-level data manipulation with pandas, including CTR computation, event denoising and deduplication, sessionization, cohort analysis, temporal joins, and performance-aware vectorized operations within the Data Manipulation (SQL/Python) domain.

  SnapchatData ScientistData Manipulation (SQL/Python)Onsite
• Compute User Retention and Analyze Event DataMedium

  This question evaluates a candidate's ability to perform SQL and Python data manipulation for event analytics, including cohort retention, aggregation of distinct purchasing users, median-of-daily-events calculations, and hourly time-series grouping.

  SnapchatData ScientistData Manipulation (SQL/Python)Onsite
• Compute same-day acceptance metrics last weekMedium

  This question evaluates proficiency in time-based data manipulation, SQL JOINs and aggregations, handling NULLs and missing reference rows, and computing acceptance and percentage metrics with careful UTC date boundary treatment.

  SnapchatData ScientistData Manipulation (SQL/Python)Technical Screen
• Count Users Whose First Daily Event Is App LaunchMedium

  Write a PostgreSQL query to identify each user's first event per calendar day and aggregate the distinct user count by operating system and device when that first event is an app launch. Tests window functions, tie-breaking logic, and multi-dimensional grouping.

  SnapchatData ScientistData Manipulation (SQL/Python)Technical Screen
• Build a cohort dashboard with Streamlit and SQLMedium

  This question evaluates proficiency in cohort analysis using SQL window functions, time-based aggregation, ARPU calculation, and integration of data querying with a Streamlit visualization front end, including handling timezones and data-quality checks.

  SnowflakeData ScientistData Manipulation (SQL/Python)Onsite
• Design an analytic warehouse for event dataMedium

  This question evaluates a candidate's competency in data warehousing and engineering for analytics, including event-level schema and partitioning, slowly changing dimensions, idempotent ingestion and deduplication, derived metrics modeling, and query performance and cost trade-offs using SQL/Python.

  SnowflakeData ScientistData Manipulation (SQL/Python)Onsite
• Query seven-day conversion with windows and dedupeMedium

  This question evaluates proficiency in time-windowed aggregations, event deduplication, and window-function–based user-level analytics across SQL and Pandas workflows.

  SnowflakeData ScientistData Manipulation (SQL/Python)Technical Screen
• Query Top Played Tracks Globally And By CountryEasy

  Tests writing clean SQL queries with aggregations, window functions, and deterministic tie-breaking to extract top playback metrics globally and per country.

  SpotifyMachine Learning EngineerData Manipulation (SQL/Python)Technical Screen
• Compute costs with validation and sorting in PythonMedium

  This question evaluates proficiency in data validation, numeric robustness, error handling, aggregation, per-item cost computation and deterministic sorting within Python-based data manipulation tasks, and it belongs to the Data Manipulation (SQL/Python) domain.

  StripeSoftware EngineerData Manipulation (SQL/Python)Technical Screen
• Design an idempotent SQL ETL for late dataMedium

  This question evaluates practical skills in idempotent ETL design, deduplication by natural keys, handling late-arriving and out-of-order records, upsert/merge strategies, and SQL-based aggregation for daily metrics.

  StripeData ScientistData Manipulation (SQL/Python)Technical Screen
• Design metrics and write SQL for a caseMedium

  This question evaluates the ability to define time‑bounded, attributable conversion metrics and to implement joins, aggregations, and diagnostic checks in SQL to measure marketing outreach impact.

  StripeData ScientistData Manipulation (SQL/Python)Technical Screen
• Design payment-to-invoice matcher with prioritiesMedium

  This question evaluates parsing and data modeling skills, deterministic matching algorithms with prioritization and tie-breaking, handling monetary values as integer cents, and the ability to design comprehensive test suites for edge cases.

  StripeSoftware EngineerData Manipulation (SQL/Python)Technical Screen
• Reconcile Monthly Bank-Account Balances Across Two DatasetsMedium

  Implement a reconciliation function that joins internal ledger balances with external bank statements for a target month, categorizing discrepancies while strictly distinguishing between missing records and zero balances.

  StripeSoftware EngineerData Manipulation (SQL/Python)Technical Screen
• Write SQL for snapshot features and labelsMedium

  This question evaluates proficiency in SQL-based feature engineering for time-series and user-event data, covering snapshotting, temporal joins, deduplication, windowed aggregations, distinct counting, and label construction in the Data Manipulation (SQL/Python) domain.

  StripeData ScientistData Manipulation (SQL/Python)Technical Screen
• Write SQL to detect recurring non-subscription usersMedium

  This question evaluates a candidate's ability to perform advanced SQL data manipulation and temporal pattern detection in transactional datasets, including attribute-based matching (amount and card fingerprint) and filtering by transaction status.

  StripeData ScientistData Manipulation (SQL/Python)Online Assessment
• Write SQL to monitor weekly chargeback spikesMedium

  This question evaluates proficiency in PostgreSQL time-series analytics, specifically aggregations, joins across payments and chargebacks, handling late-arriving events, and calculating per-week metrics such as GMV, active merchants, and chargeback rates.

  StripeData ScientistData Manipulation (SQL/Python)Onsite
• Compute per-player daily cumulative games with windowsMedium

  This question evaluates proficiency with SQL window functions, aggregation, date-aware grouping and joins for computing per-player daily counts and running totals, and it falls under Data Manipulation (SQL/Python) for Data Scientist roles, emphasizing practical application rather than purely theoretical concepts.

  TencentData ScientistData Manipulation (SQL/Python)Technical Screen
• Compute nearest index within threshold after walking distancesMedium

  This question evaluates a candidate's proficiency in data manipulation, numerical computing with NumPy, spatial reasoning for Euclidean distance and interpolation along polylines, and designing time- and space-efficient algorithms for large-scale inputs.

  TeslaMachine Learning EngineerData Manipulation (SQL/Python)Technical Screen
• Implement and vectorize NumPy Conv2DMedium

  This question evaluates understanding of 2D convolution mechanics, multidimensional NumPy array manipulation, and the competency to optimize numerical computations for performance and memory.

  TeslaMachine Learning EngineerData Manipulation (SQL/Python)Technical Screen
• Write SQL for content-view analyticsEasy

  This question evaluates proficiency in SQL-based data manipulation and analytics, including aggregation, distinct counting, NULL handling, timestamp/hour extraction, device grouping, and row-level ordering, and is situated in the Data Manipulation (SQL/Python) domain.

  The New York TimesData AnalystData Manipulation (SQL/Python)Technical Screen
• Compare list/dict; parse JSON/CSV at scaleMedium

  This question evaluates understanding of Python data structures (list vs dict), algorithmic time and memory complexity, ordering guarantees in CPython, large-scale parsing and streaming of JSON/CSV, and robust data-cleaning and error-handling strategies.

  ThumbtackData ScientistData Manipulation (SQL/Python)Onsite