Data Engineer Interview Questions

Showing 91–120 of 129 results

• 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
• Otter.AiMedium·Data Engineer
  Find the Highest-Paid Employee in Each Department

  Work through a tie-aware PostgreSQL problem that finds every highest-paid employee in each department. It assesses relational reasoning, correct joins, duplicate-name handling, and precise output semantics without assuming a single winner.

  Data Manipulation (SQL/Python)Technical Screen
• PPatreonEasy·Data Engineer
  Design 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
• PinterestMedium·Data Engineer
  Settle Shared-Expense Debts, Then Reduce the Number of Payments

  A coding question about settling shared expenses: turn a list of debts between people into payments that leave everyone even, first with no limit on the number of payments and then with as few payments as possible. It tests net-balance reasoning, matching payers with receivers, heap usage, and judging whether a greedy settlement is truly minimal.

  Software Engineering FundamentalsTechnical Screen
• PinterestMedium·Data Engineer
  Settle a Group Tab With the Fewest Possible Payments

  Turn a list of recorded debts between friends into a list of payments that settles every balance, first with any valid list and then with the fewest payments possible, using net-balance accounting and a backtracking search over who pays whom.

  Software Engineering FundamentalsTechnical Screengraphsbacktrackinggreedy
• Point72Hard·Data Engineer
  Describe Resolving Stakeholder Trade-Offs

  This question evaluates stakeholder management, prioritization, trade-off analysis, and cross-functional communication skills within a data engineering context.

  Behavioral & LeadershipTechnical Screen
• Point72Hard·Data Engineer
  Design 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
• Point72Hard·Data Engineer
  Explain Spark Execution and Optimization

  This question evaluates a candidate's understanding of the Apache Spark execution model, lazy evaluation, distributed processing (including jobs, stages, tasks, and shuffles), and performance-tuning considerations for batch and streaming pipelines on columnar data lakes.

  Software Engineering FundamentalsTechnical Screen
• Point72Hard·Data Engineer
  Implement Election Report and Banking Pipeline

  This multipart question evaluates data engineering competencies including SQL report design for complex aggregation and ranking, plus PySpark-based ETL and transaction-level analytics.

  Coding & AlgorithmsOnline Assessment
• Point72Hard·Data Engineer
  Implement Portfolio Trading Optimizer

  This question evaluates algorithmic problem-solving, array processing, input validation via decorators, and the ability to produce concise transaction reports for maximizing non-overlapping buy/sell profits.

  Coding & AlgorithmsTechnical Screen
• Point72Easy·Data Engineer
  Solve SQL and PySpark Data Tasks

  This question tests your ability to handle data cleaning, filtering, and aggregation using both SQL and PySpark syntax on a transaction dataset.

  Coding & AlgorithmsOnline AssessmentSQLPySparkData-Cleaning
• RRbcroyalbankEasy·Data Engineer
  Describe ETL and pipeline challenges

  An RBC Data Engineer co-op/intern technical screen combining behavioral and technical prompts: self-introduction, skills from a master's program, handling ETL schema differences, ensuring data integrity, and a STAR story about debugging a pipeline failure. The model answer gives concrete frameworks for schema evolution, multi-stage integrity checks, and root-cause analysis.

  Behavioral & LeadershipTechnical Screen
• RRbcroyalbankEasy·Data Engineer
  Explain ETL schema changes and ensure integrity

  This question evaluates proficiency in handling ETL schema evolution, maintaining end-to-end data integrity (accuracy, completeness, consistency, timeliness), pipeline reliability, and leadership in incident response, and is commonly asked to gauge experience managing schema drift, enforcing data quality, and owning production pipelines.

  Behavioral & LeadershipTechnical Screen
• RRbcroyalbankEasy·Data Engineer
  Explain pandas and SQL basics

  This question evaluates familiarity with pandas data structures (Series vs DataFrame), SQL filtering semantics (WHERE vs HAVING), and practical SQL aggregation for detecting duplicate records, assessing competencies in data manipulation, aggregation, and basic data quality checks.

  Data Manipulation (SQL/Python)Technical Screen
• RobinhoodHard·Data Engineer
  Aggregate user logs into 30-minute sessions

  This question evaluates data engineering competencies in time-series sessionization, datetime parsing and arithmetic, grouping and aggregation, distinct-count computation, and CSV log processing.

  Coding & AlgorithmsTechnical Screen
• RobinhoodHard·Data Engineer
  Write SQL to analyze CA residency history

  This question evaluates a candidate's ability to write SQL for temporal state reconstruction, including correct joins on composite keys, interpreting edit/audit logs, and reasoning about inclusive/exclusive timestamps.

  Software Engineering FundamentalsTechnical Screen
• ShopifyMedium·Data Engineer
  Choose 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
• ShopifyMedium·Data Engineer
  Use AI Responsibly During SQL Pair Programming

  Practice an AI-enabled SQL pair-programming workflow that keeps your reasoning visible. Cover requirement clarification, safe tool boundaries, incremental query design, independent edge-case testing, collaboration, and accountable debugging.

  Software Engineering FundamentalsTechnical Screen
• ShopifyEasy·Data Engineer
  Write SQL for session analytics

  This problem tests your ability to clean event-based log data, perform time-series aggregation, and calculate session-level metrics using SQL window functions.

  Coding & AlgorithmsTechnical ScreenSQLWindow FunctionsData Cleaning
• StripeMedium·Data Engineer
  Compute shipping cost with tiered pricing

  Evaluates implementation-level algorithmic logic for tiered pricing, covering per-unit aggregation, interval reasoning, and mixed incremental vs fixed pricing models within the Coding & Algorithms domain and Data Engineering role; abstraction level is practical, implementation-focused business logic and data transformation.

  Coding & AlgorithmsTechnical Screen
• TemuEasy·Data Engineer
  Compute User Login Streaks

  Tests the ability to write a SQL query to identify consecutive daily login streaks from raw event logs, requiring date deduplication, window functions, and streak length aggregation.

  Coding & AlgorithmsTechnical ScreenSQLWindow FunctionsTime Series
• TeslaMedium·Data Engineer
  Build a Transaction CSV Cleaning Pipeline

  This problem tests your ability to design a robust, deterministic data cleaning pipeline that handles input parsing, data validation, and precision-safe numeric handling.

  Software Engineering FundamentalsTechnical ScreenETLData EngineeringValidation
• TeslaMedium·Data Engineer
  Write SQL Data Transformation Queries

  This question tests the ability to handle window functions for running totals and conditional aggregation to transform raw manufacturing logs into formatted monthly production reports.

  Coding & AlgorithmsTechnical ScreenSQLWindow FunctionsAggregation
• TikTokMedium·Data Engineer
  Debug a Hive Query for DAU

  This question evaluates proficiency in Hive/SQL query formulation, data partitioning and pruning, join and aggregation semantics, timestamp handling, and performance tuning for large-scale analytics.

  Data Manipulation (SQL/Python)Technical Screen
• TikTokMedium·Data Engineer
  Debug a Hive insert query

  This question evaluates a candidate's ability to debug Hive INSERT/SELECT operations, focusing on schema and column alignment, data types and casts, partition handling, file formats/SerDe, ACID/transactional constraints, and syntax or reserved keyword issues.

  Data Manipulation (SQL/Python)Technical Screen
• TikTokMedium·Data Engineer
  Describe a data-heavy project

  Describe a data-heavy project 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
• TikTokHard·Data Engineer
  Design 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
• TikTokMedium·Data Engineer
  Explain Your Resume and Behavioral Examples

  This question evaluates behavioral and leadership competencies within data engineering, focusing on impact measurement, decision-making under ambiguity, incident response and mitigation, stakeholder management, prioritization under schedule changes, and mission alignment for public-sector contexts.

  Behavioral & LeadershipTechnical Screen