Data Engineer Interview Questions

Showing 31–60 of 91 results

• GoogleMedium·Data Engineer
  Write SQL and merge linked lists

  Solve a SQL customer order aggregation task and merge two sorted linked lists. The solution uses CTEs, window functions, LEFT JOINs, COALESCE, latest-order tie handling, optional returned-order filtering, and an O(m+n) dummy-head linked-list merge.

  Coding & AlgorithmsTechnical Screen
• IBInteractive BrokersMedium·Data Engineer
  Compute Minimum Production Days

  A data engineering coding screen on greedy scheduling under a cooldown constraint: produce an ordered list of products, one per day, where each product type must cool down for `space` days before it can be produced again. Candidates must return the minimum number of days, ideally in O(n) time using a hash map of last-produced days.

  Coding & AlgorithmsTechnical Screen
• JGJain GlobalMedium·Data Engineer
  Build a Self-Service Market Data Discovery and Support Layer

  This problem tests your ability to design a robust metadata-driven orchestration layer that manages data lineage, access control, and quality monitoring for financial market datasets.

  Software Engineering FundamentalsTechnical ScreenSystem DesignData EngineeringMetadata
• JGJain GlobalMedium·Data Engineer
  Design an Incremental Rolling-Metrics Data Pipeline

  Design an incremental batch data pipeline to compute multi-horizon rolling financial metrics over daily ticker observations, handling bounded historical restatements, atomic publication, and data reconciliation.

  System DesignTechnical ScreenData EngineeringData PipelinesFinancial Data
• JGJain GlobalMedium·Data Engineer
  Design and Debug a Point-in-Time Backtest Data Pipeline

  This problem tests the ability to architect a robust financial data pipeline that ensures point-in-time accuracy and reproducible research.

  System DesignTechnical ScreenData EngineeringFinancial DataBacktesting
• JGJain GlobalMedium·Data Engineer
  Model Effective-Dated Compliance Restrictions for Securities

  This question tests the ability to design a time-series database schema capable of handling bitemporal data, identifier changes, and overlapping compliance restrictions.

  System DesignTechnical ScreenData ModelingTime-SeriesBitemporal
• LinkedInMedium·Data Engineer
  Implement a stack supporting max and popMax

  This question evaluates understanding of data structures and algorithmic complexity, focusing on designing a stack variant that supports efficient retrieval and removal of the current maximum while preserving stack behavior.

  Coding & AlgorithmsTechnical Screen
• MetaHard·Data Engineer
  Compute Max Score From Up to 3 Categories

  This Coding & Algorithms question in the Data Engineer domain evaluates grouping and top‑k aggregation concepts, testing the ability to combine per-category aggregation with selection under a uniqueness constraint.

  Coding & AlgorithmsTechnical Screen
• MetaMedium·Data Engineer
  Compute capacities after site closures

  This question evaluates a candidate's ability to manipulate nested dictionary data structures and perform aggregate capacity computations, assessing data transformation, mapping, and set-based reasoning skills relevant to a Data Engineer within the Coding & Algorithms domain.

  Coding & AlgorithmsTechnical Screen
• MetaMedium·Data Engineer
  Compute reservation diff for largest member

  This question evaluates competency in relational data manipulation and aggregation using SQL or Python, including joins, grouping, and handling missing referrer relationships.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Engineer
  Define metrics and data model for product features

  This problem evaluates your ability to translate product goals into actionable metrics and design a scalable, reliable data schema to support those metrics.

  System DesignTechnical ScreenData ModelingProduct AnalyticsMetrics
• MetaHard·Data Engineer
  Design tables for event-driven metrics

  This question evaluates relational data modeling and data engineering competencies for high-volume event analytics, including schema design, partitioning and clustering strategies, duplicate and late-arriving event handling, and normalization versus denormalization trade-offs.

  System DesignTechnical Screen
• MetaMedium·Data Engineer
  Measure Game Monetization and Validate a Reporting Repair

  Define gaming monetization metrics, separate business changes from data failures, and validate repairs with reconciliation and guardrails.

  Analytics & ExperimentationTechnical Screen
• MetaMedium·Data Engineer
  Return count and renewal percentage of unreturned good copies

  This question evaluates proficiency in SQL data manipulation, including conditional filtering, NULL handling, aggregation, and computing conditional percentages across related tables.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Engineer
  Solve Python and SQL data tasks

  This question evaluates data manipulation and engineering competencies by testing Python proficiency with nested data structure handling, complexity analysis and test-case design, alongside SQL proficiency in UTC-aware aggregation and per-user deduplication to compute daily active users; it is categorized as Data Manipulation (SQL/Python) within the data engineering domain. Such problems are commonly asked to assess practical implementation ability and robustness under edge cases, as well as conceptual understanding of time/space complexity and correct date-time handling, reflecting a primarily practical application with elements of conceptual analysis.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Engineer
  Solve SQL and Python coding tasks

  This question evaluates SQL proficiency in relational querying (joins, filtering, aggregation) and Python algorithmic problem-solving for non-adjacent selection, measuring a data engineer's ability to manipulate relational data and implement efficient algorithms.

  Coding & AlgorithmsTechnical Screen
• MetaMedium·Data Engineer
  Solve library coding tasks in Python

  Solve library coding tasks in Python evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Coding & AlgorithmsTechnical Screen
• MetaMedium·Data Engineer
  Validate alternating checkout/return logs

  This question evaluates the ability to process sequential event logs, maintain per-entity state invariants (checkout vs return), and reason about algorithmic time and space complexity within a validation task.

  Coding & AlgorithmsTechnical Screen
• MetaMedium·Data Engineer
  Write SQL for library analytics

  This question evaluates proficiency in SQL-based data manipulation and analytical querying, focusing on relational schema navigation, joins, aggregation, filtering, and handling temporal and conditional metrics within a library dataset.

  Data Manipulation (SQL/Python)Technical Screen
• MetaMedium·Data Engineer
  Write queries for follows and bookings

  This problem tests your ability to manage relational data, handle temporal overlaps for booking availability, and derive insights from social graph-like connections using SQL or data manipulation scripts.

  Coding & AlgorithmsTechnical ScreenSQLIntervalsGraphs
• NVIDIAMedium·Data Engineer
  Data Pipeline Reliability, Backfills, and Spark Optimization

  Prepare for a NVIDIA software engineering interview question about data pipeline reliability, backfills, and spark optimization. The prompt highlights practical reasoning, edge cases, trade-offs, and validation criteria without revealing the model answer.

  Software Engineering FundamentalsTechnical Screen
• NVIDIAMedium·Data Engineer
  Design a GPU Telemetry Collection and Analytics Platform

  Prepare for a NVIDIA system design interview about design a gpu telemetry collection and analytics platform. This question focuses on requirements, architecture, trade-offs, reliability, and follow-up discussion areas.

  System DesignTechnical Screen
• NVIDIAMedium·Data Engineer
  Merge Overlapping Time Intervals

  Practice a NVIDIA coding interview problem focused on merge overlapping time intervals. The prompt emphasizes edge cases, clean implementation, and verifiable test behavior without revealing the solution.

  Coding & AlgorithmsTechnical Screen
• NetflixHard·Data Engineer
  Implement a Weighted Eviction Cache

  This problem tests your ability to design a cache that respects both memory capacity and item weights, requiring an efficient combination of a hash map and a doubly linked list.

  Coding & AlgorithmsTechnical ScreenHash MapDoubly Linked ListLRU Cache
• NextdoorHard·Data Engineer
  Design KPI dashboard tables: retention, weekly rollups, timezone

  This Nextdoor data engineer system design screen asks you to design the analytical data model and incremental ETL behind a fast, large-scale KPI dashboard for a photo-sharing app. It covers derived aggregate table design (grain, keys, partitioning), DAU/new-user/new-photo and follower-distribution metrics, 7-day deltas, then extends to week-1 cohort retention, weekly rollups, and switching the reporting timezone. The focus is correctness, performance at scale, and maintainability with timezone handled as a reporting parameter.

  System DesignTechnical Screen
• NextdoorHard·Data Engineer
  Write SQL for app metrics

  This question evaluates proficiency in SQL-based data engineering, focusing on time-zone-aware timestamp handling, aggregation of event and relationship data, cohort retention analysis, and computation of product metrics like DAU, new users, photo activity, and follower distributions.

  Coding & AlgorithmsTechnical Screen
• NextdoorHard·Data Engineer
  Write SQL for basic user metrics

  This question evaluates proficiency in SQL for relational databases, specifically testing aggregation, joins, date/time grouping, and handling tied results when computing user metrics such as total users, peak signup month, and top-followed users.

  Coding & AlgorithmsTechnical Screen
• NuroEasy·Data Engineer
  Compute unique duration by merging intervals

  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.

  Data Manipulation (SQL/Python)Technical Screen
• Otter.AiMedium·Data Engineer
  Defend a Data Pipeline Architecture and Its Trade-offs

  Prepare to defend a production data pipeline you genuinely built or changed. The prompt tests how you connect requirements to architecture, compare alternatives, explain replay and backfills, diagnose a hard failure, quantify outcomes, and reflect on ownership and trade-offs.

  System DesignTechnical Screen
• Otter.AiMedium·Data Engineer
  Find Every Project with the Most Employees

  Build a PostgreSQL aggregation that returns every project tied for the largest employee count. This compact exercise checks grouping, maximum selection, tie preservation, empty-input behavior, and careful use of primary-key guarantees.

  Data Manipulation (SQL/Python)Technical Screen