Data Manipulation (SQL/Python) interview questions

Showing 271–300 of 446 results

• Implement paginated API ingestionMedium

  This question evaluates skills in API integration, paginated data ingestion, error handling with retries and exponential backoff, rate limiting, streaming CSV serialization without holding all records in memory, schema normalization, unit testing, and time/space complexity analysis.

  MillenniumSoftware EngineerData Manipulation (SQL/Python)Online Assessment
• Calculate First-Week Retention by Signup CohortMedium

  Compute first-week activity retention by signup-week cohort with all cohort users in the denominator and zero-retention cohorts included.

  MixpanelData ScientistData Manipulation (SQL/Python)Technical Screen
• Compare First-Week Retention by Plan TypeMedium

  Compare first-week retention by recorded plan type with distinct-user counts, inclusive denominators, and four-decimal rates.

  MixpanelData ScientistData Manipulation (SQL/Python)Technical Screen
• Count Users Active After SignupMedium

  Count distinct users with at least one event on or after signup using a PostgreSQL query.

  MixpanelData ScientistData Manipulation (SQL/Python)Technical Screen
• Count Users Retained on Day SevenMedium

  Count users active on the seventh calendar day after signup, without double-counting repeated events.

  MixpanelData ScientistData Manipulation (SQL/Python)Technical Screen
• Find top advertisers and cost per KPIMedium

  This question evaluates proficiency in data manipulation and analytical SQL/Python skills, including aggregation, ranking with ties, dataset joins at a defined grain, date-based filtering relative to campaign launch dates, and handling division-by-zero scenarios when computing cost metrics.

  MolocoData ScientistData Manipulation (SQL/Python)Technical Screen
• Describe Your Analysis and Visualization ToolkitMedium

  Tests your ability to articulate an end-to-end data analysis and visualization workflow clearly and concisely for recruiters and hiring managers, connecting technical tool choices directly to business outcomes.

  Morgan StanleyData ScientistData Manipulation (SQL/Python)HR Screen
• Fetch and parse JSON from REST APIMedium

  A Motive Software Engineer onsite coding exercise: build a small, resilient script from scratch that performs an authenticated HTTP GET against a paginated REST API, parses the JSON, extracts and transforms records, and produces both serialized CSV/JSON output and per-category top-k aggregates. It evaluates HTTP/REST integration, rate-limit and retry handling with backoff, timeouts, malformed-record handling, unit testing, logging, and complexity analysis.

  MotiveSoftware EngineerData Manipulation (SQL/Python)Onsite
• Analyze and debug Python utilitiesMedium

  This interview question evaluates SQL or pandas logic, joins, grouping, window functions, null handling, edge cases, and validation in a realistic interview setting. A strong answer for Analyze and debug Python utilities states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  NVIDIASoftware EngineerData Manipulation (SQL/Python)Onsite
• Implement 2D convolution using NumPy slicingMedium

  This question evaluates a candidate's proficiency with array manipulation and vectorized numerical computation using NumPy, focusing on 2D convolution concepts such as slicing, broadcasting, and filter application.

  NVIDIASoftware EngineerData Manipulation (SQL/Python)Technical Screen
• Implement a Python test harnessMedium

  This interview question evaluates SQL or pandas logic, joins, grouping, window functions, null handling, edge cases, and validation in a realistic interview setting. A strong answer for Implement a Python test harness states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  NVIDIASoftware EngineerData Manipulation (SQL/Python)Online Assessment
• Aggregate D1 retention cohorts in SQLMedium

  This question evaluates a candidate's competency in time-series cohort analysis and SQL-based data manipulation, specifically measuring DAU, new-user cohorts, D1 retention and revenue-per-user while handling deduplication and user filtering.

  NetflixData ScientistData Manipulation (SQL/Python)Onsite
• Analyze Retention Metrics Using SQL and PythonMedium

  This question evaluates proficiency in SQL time-based cohort analysis and practical Python algorithm implementation, testing retention metric computation using windowed date calculations and an efficient approach for finding two numbers whose sum is closest to zero.

  NetflixData ScientistData Manipulation (SQL/Python)Other
• Determine Maximum Consecutive Order Days Per UserMedium

  Determine Maximum Consecutive Order Days Per User 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.

  NetflixData ScientistData Manipulation (SQL/Python)Onsite
• Transform flat keys into nested dictionaryMedium

  This question evaluates proficiency in parsing and restructuring hierarchical parameter keys into nested dictionary structures, string manipulation for grouping, handling nonconforming keys and duplicates, and reasoning about time and space complexity.

  NetflixMachine Learning EngineerData Manipulation (SQL/Python)Technical Screen
• Write SQL for rolling frequency capsMedium

  This question evaluates proficiency with rolling-window analytics, frequency cap logic, time-based attribution, and per-user/per-campaign aggregation for advertising data.

  NetflixData ScientistData Manipulation (SQL/Python)Onsite
• Analyze Document Collaboration CSVsMedium

  This problem tests your ability to clean, join, and analyze messy user-activity data using pandas or SQL, focusing on window functions and handling missing values.

  NotionData ScientistData Manipulation (SQL/Python)Technical Screen
• Analyze document collaboration from CSV logsEasy

  This question evaluates data manipulation and analytical competencies—particularly deduplication, joins, aggregation, co-occurrence counting, and metric definition—applied to CSV access logs and user metadata.

  NotionData ScientistData Manipulation (SQL/Python)Technical Screen
• Analyze document collaboration patternsMedium

  This question evaluates a candidate's competency in data manipulation and analytics—specifically joins, deduplication of user-document pairs, aggregation, and computation of user- and country-level collaboration metrics using SQL or Python.

  NotionData ScientistData Manipulation (SQL/Python)Technical Screen
• Compute unique duration by merging intervalsEasy

  This question evaluates a data engineer's proficiency in temporal data manipulation and interval-set reasoning using SQL and Python, specifically skills in merging overlapping intervals and computing deduplicated cumulative durations.

  NuroData EngineerData Manipulation (SQL/Python)Technical Screen
• Compute churn with re-subscriptionsHard

  This problem tests your ability to reconstruct user subscription states over time from raw event logs, specifically identifying active status to calculate monthly churn rates.

  OpenAIData ScientistData Manipulation (SQL/Python)Technical Screen
• Compute signup rate and retention from raw logsEasy

  This question evaluates the ability to manipulate event-level logs to compute cohort metrics—specifically signup rate and D30 retention—testing proficiency in SQL/Python, event-time windowing, joins, aggregation, and understanding of upstream logging semantics.

  OpenAIData ScientistData Manipulation (SQL/Python)Technical Screen
• Debug and harden trial-assignment Python codeMedium

  This question evaluates understanding of experiment assignment, data integrity, reliability engineering, concurrency, observability, and privacy in data systems, testing competencies around randomness, idempotency, time semantics, retries, and failure-mode identification.

  OpenAIData ScientistData Manipulation (SQL/Python)Technical Screen
• Design Schema and Logic for Subscription Event TrackingMedium

  This question evaluates proficiency in event-driven data modeling, temporal state derivation, and implementation of SQL/Python logic to compute per-user subscription status and history within the Data Manipulation (SQL/Python) domain.

  OpenAIData ScientistData Manipulation (SQL/Python)Online Assessment
• Design Schema for Accurate Subscription State TrackingMedium

  This question evaluates proficiency in temporal schema design and event-level data modeling, focusing on reconstructing time-varying subscription state using SQL and Python in the Data Manipulation (SQL/Python) domain.

  OpenAIData ScientistData Manipulation (SQL/Python)Online Assessment
• Handle repeated churn in SQLHard

  This problem tests your ability to handle complex temporal data by identifying user churn and re-subscription events, then calculating metrics like tenure and churn count per user.

  OpenAIData ScientistData Manipulation (SQL/Python)Technical Screen
• Implement vectorized NumPy ops and explain broadcastingMedium

  This question evaluates proficiency in vectorized numerical computing with NumPy, understanding of broadcasting semantics, and awareness of numerical stability and algorithmic time/space complexity when manipulating high-dimensional arrays.

  OpenAIMachine Learning EngineerData Manipulation (SQL/Python)Onsite
• Parse and build binary data in PythonMedium

  This question evaluates proficiency in binary data handling and serialization/deserialization in Python, including little-endian integer encoding, length-prefixed payloads, robust error handling for short reads and invalid lengths, and testability and performance considerations.

  OpenAISoftware EngineerData Manipulation (SQL/Python)Technical Screen
• Train and analyze a classifierMedium

  This question evaluates proficiency in end-to-end machine learning engineering, including exploratory data analysis, time-aware train/validation/test splitting, model baseline and improvement, class imbalance strategies, leakage-safe hyperparameter tuning, metric computation and calibration, error analysis, reproducible training pipelines with CLI/config/seed control, explainability (feature importance/SHAP and ablations), and documentation of risks, fairness checks and monitoring hooks. It is commonly asked to assess practical ability to manage the full ML lifecycle and data hygiene in production-like scenarios, testing applied Data Manipulation (SQL/Python) and Machine Learning competencies with an emphasis on practical application while also requiring conceptual understanding of evaluation, calibration and fairness.

  OpenAIMachine Learning EngineerData Manipulation (SQL/Python)Technical Screen
• Write SQL for repeat churnHard

  This question evaluates proficiency in event-time analytics and cohort churn metric computation using SQL, focusing on correct identification of first qualifying events and aggregation by experimental variant.

  OpenAIData ScientistData Manipulation (SQL/Python)Technical Screen