Data Engineer Interview Questions

Showing 1–30 of 91 results

• AgodaEasy·Data Engineer
  Count Integers That Appear Exactly Once

  Count distinct integers with frequency exactly one using complete occurrence counts, including empty arrays and separated duplicates.

  Coding & AlgorithmsTechnical Screen
• 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
• AmazonEasy·Data Engineer
  Answer Amazon Behavioral Questions

  Three classic Amazon final-round behavioral questions — a significant challenge you overcame, motivating a struggling team, and why Amazon — answered with the STAR framework and Amazon Leadership Principles. Each answer emphasizes ownership, problem solving, collaboration, and quantified impact.

  Behavioral & LeadershipTechnical Screen
• AmazonMedium·Data Engineer
  Describe past NLP work and collaboration

  This question evaluates a candidate's technical expertise in applied NLP methods, data engineering competencies, and collaborative leadership in managing annotation workflows, along with their ability to articulate specific contributions, trade-offs, and metrics from past projects.

  Behavioral & LeadershipTechnical Screen
• AmazonEasy·Data Engineer
  Solve Two String Problems

  This task evaluates string manipulation and encoding for generating unique Morse representations, along with sentence segmentation and combinatorial enumeration for producing all valid word-break sentences, focusing on transformation, deduplication, and decomposition skills.

  Coding & AlgorithmsTechnical Screen
• AppleMedium·Data Engineer
  Design TikTok Data Engineering Systems

  This problem tests the ability to design an end-to-end data pipeline for high-throughput mobile app event logging, focusing on ingestion, transformation, and storage optimization.

  System DesignTechnical ScreenData PipelinesDistributed SystemsOLAP
• AppleHard·Data Engineer
  Discuss Challenges and Career Goals

  This question evaluates problem-solving, technical depth, communication, leadership, and career-motivation competencies relevant to a Data Engineer role within the Behavioral & Leadership category.

  Behavioral & LeadershipTechnical Screen
• AppleHard·Data Engineer
  Encode and Rebuild a Binary Tree

  This question evaluates skills in data structure serialization and deserialization, binary tree representation, and algorithmic design for deterministic data formats.

  Coding & AlgorithmsTechnical Screen
• AppleEasy·Data Engineer
  Implement most_frequent_key without using max()

  This question evaluates proficiency in Python fundamentals and object-oriented inheritance, focusing on frequency aggregation using a precomputed dictionary, iteration over mappings, and correct handling of ties and empty inputs.

  Coding & AlgorithmsTechnical Screen
• BasetenMedium·Data Engineer
  Design a Data Pipeline for Messy Pod Alerts and Bad Pod Detection

  Prepare for a Baseten system design interview about design a data pipeline for messy pod alerts and bad pod detection. This question focuses on requirements, architecture, trade-offs, reliability, and follow-up discussion areas.

  System DesignTechnical Screen
• BBlackRockMedium·Data Engineer
  Solve two interval array problems

  This question evaluates proficiency with array and interval-manipulation concepts—specifically sorting, merging overlapping ranges, and robust edge-case handling in interval operations.

  Coding & AlgorithmsTechnical Screen
• BloombergMedium·Data Engineer
  Implement RotatingFileSink in hierarchy

  This question evaluates a candidate's proficiency in Python object-oriented design, file I/O and JSON serialization, concurrency control via locking, context manager protocols, custom exception handling, and adherence to Liskov substitution when implementing a rotating file sink.

  Software Engineering FundamentalsTechnical Screen
• BloombergMedium·Data Engineer
  Implement classes within an abstract Python framework

  This question evaluates a candidate's skills in object-oriented design, implementing concrete classes from abstract interfaces, file I/O and streaming data transformations, error handling, and integration with a registry and CLI within a Python codebase.

  Coding & AlgorithmsTechnical Screen
• BloombergMedium·Data Engineer
  Write PostgreSQL string-manipulation query

  This question evaluates proficiency in PostgreSQL string manipulation, regular expressions, URL parsing, key-value parsing and timestamp-based filtering for data transformation tasks.

  Data Manipulation (SQL/Python)Technical Screen
• BByteDanceHard·Data Engineer
  Maximize watch time under adjacency constraint

  This Coding & Algorithms question for a Data Engineer role evaluates dynamic programming and sequence-optimization skills by requiring selection of a subsequence under a pairwise adjacency constraint on video durations, at a moderate-to-advanced algorithmic abstraction level.

  Coding & AlgorithmsTechnical Screen
• Capital OneMedium·Data Engineer
  Answer conflict and ambiguity with STAR stories

  This question evaluates a candidate's conflict-resolution, cross-functional communication, technical troubleshooting, ambiguity-management, and reflective learning competencies within a data engineering context.

  Behavioral & LeadershipTechnical Screen
• Capital OneMedium·Data Engineer
  Deep-dive XGBoost handling and overfitting

  This question evaluates proficiency with gradient-boosted decision trees and related competencies such as native versus imputation handling of missing values, causes and control of overfitting via regularization and hyperparameters, selection of metrics and validation strategies for imbalanced outcomes, and practical debugging concerns like data leakage, time-based splits, and calibration for a Data Engineer role. It is commonly asked in Machine Learning interviews to assess both conceptual understanding of algorithm behavior and practical application of model evaluation and deployment-ready validation techniques.

  Machine LearningTechnical Screen
• Capital OneMedium·Data Engineer
  Evaluate credit-limit increase profitability

  This question evaluates a data engineer's competency in business analytics, unit-economics P&L modeling, risk-versus-revenue trade-off analysis, and experiment-driven evaluation of credit product changes.

  System DesignTechnical Screen
• Capital OneMedium·Data Engineer
  Explain production model drop to a PM

  This question evaluates a candidate's ability to explain production model degradation, translate technical diagnostics into non-technical language, and articulate trade-offs between user impact and delivery timelines.

  Behavioral & LeadershipTechnical Screen
• CClickhouseMedium·Data Engineer
  Answer SQL And Data Warehouse Fundamentals For A Data Engineering Interview

  This problem tests foundational knowledge of SQL set operations, performance optimization techniques for join operations, and the architectural principles behind building scalable data warehouses.

  Data Manipulation (SQL/Python)Technical ScreenSQLData WarehousingWindow Functions
• DatabricksMedium·Data Engineer
  Diagnose data quality and pipeline performance issues

  Diagnose data quality and pipeline performance issues evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Behavioral & LeadershipTechnical Screen
• FigmaMedium·Data Engineer
  Write SQL for first share and closest collaborator

  This question evaluates SQL proficiency—specifically joins, filtering, type casting, temporal aggregation, window functions or deduplication, and deterministic tie-breaking—by requiring derivation of first non-creator view times and per-creator collaborator counts from event tables.

  Coding & AlgorithmsTechnical Screen
• FFigureaiMedium·Data Engineer
  Build a Firmware Data Quality and Release-Feedback Framework

  This question tests the ability to design an end-to-end data pipeline architecture that ensures high-fidelity telemetry collection, automated quality validation, and safe release orchestration for embedded firmware devices.

  System DesignTechnical ScreenData EngineeringFirmwareObservability
• FFigureaiMedium·Data Engineer
  Choose a Metric Hierarchy for a Humanoid-Robot Fleet

  This problem tests the ability to structure a multi-layered metrics dashboard for complex hardware-software systems, balancing operational efficiency against safety and system stability.

  Analytics & ExperimentationTechnical ScreenMetricsData ModelingRobotics
• FFigureaiMedium·Data Engineer
  Design a Telemetry Platform Ingesting Two Gigabytes per Second

  This question tests your ability to design a high-throughput, fault-tolerant telemetry ingestion pipeline capable of handling high-velocity data streams from distributed robotic systems.

  System DesignTechnical ScreenData EngineeringScalabilityDistributed Systems
• FFigureaiMedium·Data Engineer
  Diagnose the Leading Cause of Slow or Failed Charging

  This problem tests your ability to define metrics from imperfect telemetry, perform root cause analysis on hardware failure, and design a strategy to identify performance bottlenecks in an autonomous charging system.

  Analytics & ExperimentationTechnical ScreenData EngineeringRoot Cause AnalysisTelemetry
• GoogleMedium·Data Engineer
  Describe building and improving a dashboard

  Prepare a Google Data Engineer behavioral answer about building and improving a stakeholder dashboard. The solution covers business goals, KPI design, data validation, BI tooling, anomaly detection, root-cause analysis, feedback handling, and measurable impact.

  Behavioral & LeadershipTechnical Screen
• GoogleMedium·Data Engineer
  User Activity Analytics: Unique Users per Activity and Average Time on Site

  This question evaluates a candidate's ability to process semi-structured event data using grouping, deduplication, and timestamp arithmetic in Python. It falls under coding and algorithms, commonly asked to assess practical data-wrangling skills such as aggregating per-key counts and computing time-based metrics while handling edge cases like unordered records and single-event users.

  Coding & AlgorithmsTechnical Screen