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Data Engineer Interview Questions
Practice 9403 coding, system design and machine learning questions from 546 companies, sorted by role, topic and interview round. Every question has step-by-step hints, a code runner that supports 8 languages and an AI mentor; members also get the full solution walkthrough.
- 9403
- Questions
- 546
- Companies
- 11
- Topics
- 8
- Languages
Showing 61–90 of 129 results
- MetaMediumData EngineerCompute 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 - MetaHardData EngineerDefine and validate product metrics
Define and validate product metrics evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite - MetaMediumData EngineerDefine 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 - MetaHardData EngineerDesign batch and streaming ETL architecture
Design batch and streaming ETL architecture evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
System DesignOnsite - MetaHardData EngineerDesign 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 - MetaHardData EngineerEvaluate impact of short videos in feed
This question evaluates competency in metrics definition, causal experimentation, product analytics, and feed system behavior within the System Design and Data Engineering domain.
System DesignOnsite - MetaMediumData EngineerMeasure 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 - MetaMediumData EngineerRecommend two-hop follows in Python
This question evaluates fluency with graph traversal, set operations, deduplication, and frequency-based ranking in Python, targeting manipulation of directed "follows" relationships represented as dictionaries and is categorized under Data Manipulation (SQL/Python).
Data Manipulation (SQL/Python)Onsite - MetaMediumData EngineerReturn 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 - MetaMediumData EngineerReturn top-3 content per category
This question evaluates the ability to perform grouped top-k selection, deterministic tie-breaking and ordering, and to reason about algorithmic efficiency when manipulating structured datasets in the Data Manipulation (SQL/Python) domain for a Data Engineer role.
Data Manipulation (SQL/Python)Onsite - MetaMediumData EngineerSolve 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 - MetaMediumData EngineerSolve 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 - MetaMediumData EngineerSolve 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 - MetaMediumData EngineerValidate 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 - MetaMediumData EngineerValidate carpool capacity
This question evaluates understanding of interval-based resource accounting and algorithmic reasoning with cumulative constraints, assessing skills in processing time-bound events and managing capacity limits.
Coding & AlgorithmsOnsite - MetaMediumData EngineerVisualize Netflix metric trends
This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Visualize Netflix metric trends states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite - MetaHardData EngineerWrite SQL for car rental utilization by city
This question evaluates proficiency in SQL querying and relational data modeling, focusing on time-interval overlap logic, distinct aggregation across joined tables, null handling, and safe computation of utilization metrics.
Coding & AlgorithmsOnsite - MetaMediumData EngineerWrite 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 - MetaMediumData EngineerWrite 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 - NVIDIAMediumData EngineerData 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 - NVIDIAMediumData EngineerDesign 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 - NVIDIAMediumData EngineerMerge 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 - NetflixHardData EngineerDesign config rollout and click aggregation
This problem tests your ability to design a global configuration distribution system combined with a high-throughput event processing pipeline to monitor rollout health.
System DesignOnsiteDistributed SystemsReal-time AnalyticsEvent Streaming - NetflixHardData EngineerExplain concurrency and reliability tradeoffs
This problem tests your understanding of balancing thread-safe resource management in a high-concurrency local environment against the systemic requirements of a distributed data pipeline.
Software Engineering FundamentalsOnsiteConcurrencyDistributed SystemsCaching - NetflixHardData EngineerImplement 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 - NetflixHardData EngineerImplement phrase search and JSON path
This problem tests your ability to process unstructured text into a searchable index and navigate hierarchical data structures using path patterns.
Coding & AlgorithmsOnsiteInverted IndexString ProcessingRecursion - NextdoorHardData EngineerDesign 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 - NextdoorHardData EngineerWrite 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 - NextdoorHardData EngineerWrite 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 - NotionMediumData EngineerExplain impact and partnership
This question evaluates a Data Engineer's behavioral and leadership competencies, including ownership, cross-functional collaboration with data scientists and partners, stakeholder communication, problem framing, impact measurement, and responsiveness to managerial feedback, and is categorized under Behavioral & Leadership.
Behavioral & LeadershipOnsite
Meta
NVIDIA
Netflix
Nextdoor
Notion