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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 1–26 of 26 results
- AppleMediumData EngineerDesign 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 - BasetenMediumData EngineerDesign 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 - ByteDanceMediumData EngineerDesign a Data Service for Downstream Consumers
Design a centralized, governed data serving platform that provides downstream consumers with stable query APIs, schema evolution safety, cursor-based exports, and data freshness metadata.
System DesignHR ScreenData EngineeringAPI DesignData Governance - Capital OneMediumData EngineerEvaluate 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 - DiscordHardData EngineerDesign Video Call Analytics Tables
This question evaluates data modeling and analytics competencies including event schema design, dimensional modeling (facts and dimensions), ETL/cleaning and deduplication, and time-windowed SQL aggregation.
System DesignOnsite - DisneyEasyData EngineerData Pipeline Design for Cloud and Hybrid On-Prem Environments on AWS and Azure
A senior data engineering design question: build a data pipeline from operational sources to analytics on one cloud, then rework it for a hybrid on-premises and cloud environment and for both AWS and Azure. It tests tool selection and trade-offs, ingestion and storage design, data quality, security and portability.
System DesignOnsite - DisneyMediumData EngineerDesign a clickstream ingestion and aggregation pipeline for billions of events per day
Design an end-to-end clickstream pipeline that ingests roughly one billion telemetry events per day from web, mobile, and TV clients and serves both near-real-time dashboards and accurate finalized daily aggregates without lagging. The question covers staged architecture and back-of-envelope sizing, a durable partitioned log for back-pressure, event-time windowing with watermarks and allowed lateness, late-arriving data and the provisional-to-finalized lifecycle, exactly-once deduplication in Flink versus Spark Structured Streaming, autoscaling on consumer lag, hot-partition key skew, high-cardinality DAU/MAU with HyperLogLog, and reprocessing after a logic bug. A common Disney data engineer online assessment system design question.
System DesignOnline Assessment - FigureaiMediumData EngineerBuild 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 - FigureaiMediumData EngineerDesign 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 - Jain GlobalMediumData EngineerDesign 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 - Jain GlobalMediumData EngineerDesign 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 - Jain GlobalMediumData EngineerModel 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 - 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 - 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 - 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 - 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 - Otter.AiMediumData EngineerDefend 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 - PatreonEasyData EngineerDesign a Data Processing System for JSON User Records: Batch vs Streaming Input
Design a data processing system for JSON user records that contain names, addresses and payment methods, and explain how the design changes when the upstream source delivers data in batches versus as a continuous stream. It tests pipeline architecture, idempotent loading, ordering and late data, and the handling of sensitive fields.
System DesignTechnical Screen - Point72HardData EngineerDesign Data Quality and Observability Pipeline
This question evaluates proficiency in designing production data orchestration, data quality validation, and observability frameworks for both batch and streaming pipelines, covering dependency management, schema and integrity checks, and operational handling of anomalous records.
System DesignTechnical Screen - ShopifyMediumData EngineerChoose Between Batch and Streaming for a Data Pipeline
Work through a data-platform design that must support fast operational metrics and reproducible daily reporting. Compare batch, streaming, and hybrid choices while addressing late events, deduplication, replay, backfills, governance, and recovery.
System DesignTechnical Screen - TikTokHardData EngineerDesign a Warehouse for Key Metrics
This question evaluates a data engineer's skills in dimensional modeling, data warehousing, and analytics infrastructure, covering facts and dimensions, grain definition, surrogate keys, partitioning and clustering strategies, and operational concerns like deduplication, late-arriving events, null/anonymous users, and slowly changing dimensions.
System DesignTechnical Screen - ZooxMediumData EngineerDesign teleoperations data models and pipelines
This question evaluates competency in data modeling, real-time and batch pipeline architecture, telemetry ingestion, metrics design, and operational monitoring for teleoperations and autonomous vehicle stability.
System DesignOnsite - xAIMediumData EngineerBuild a One-Pass Data Cleaning Pipeline
This problem tests your ability to design a memory-efficient, scalable data streaming pipeline that processes raw web-scraped documents into a high-quality dataset for model pretraining.
System DesignTechnical ScreenData EngineeringStreamingData Cleaning
Apple
Baseten
Capital One
Discord
Disney
Meta
NVIDIA
Netflix
Nextdoor
Otter.Ai
Point72
Shopify
TikTok
Zoox
xAI