Data Engineer Interview Questions

Data Engineer Interview Questions

Practice 160 real Data Engineer interview questions for 2026. Covers companies like Meta, TikTok, RBC Royal Bank, and Point72 — real questions from actual interviews with detailed solutions. This collection of Data Engineer interview questions is designed for hands‑on interview preparation: expect live SQL and Python exercises, pipeline design and debugging, and case problems that test data modeling, throughput and latency tradeoffs. What’s distinctive about data engineering interviews is the mix of coding plus systems thinking: interviewers evaluate SQL fluency and data modeling, end‑to‑end pipeline architecture (streaming, Spark, Kafka) and data reliability/observability. Meta, TikTok, RBC Royal Bank, and Point72 are actively hiring for this role and commonly test scalable ETL design, warehouse/schema design and production reliability. To prepare, practice medium‑to‑hard SQL, build a few Spark/streaming exercises, sketch architecture tradeoffs for high‑throughput pipelines, and rehearse concise behavioral stories that show ownership and incident response.

160 Questions 40 Companies08.03.2026
Showing 20 results
Role
Point72 logo
Point72
Hard
Data Engineer

Describe Resolving Stakeholder Trade-Offs

Tell me about a time when you worked with multiple stakeholders who had different needs or priorities, and you had to create a solution that satisfied...

Behavioral & Leadership
9
0
65 people solved
Apr 30, 2026
Figureai logo
Figureai
Medium
Data EngineerSenior+ Locked

Choose a Metric Hierarchy for a Humanoid-Robot Fleet

Create a metric hierarchy for a humanoid-robot fleet spanning useful task completion, autonomy, reliability, safety, charging, operations, and data he...

Analytics & Experimentation
0
0
15 people solved
May 20, 2026
Figureai logo
Figureai
Medium
Data EngineerSenior+ Locked

Build a Firmware Data Quality and Release-Feedback Framework

Design a firmware telemetry quality framework that brings data and firmware teams together around versioned contracts, pre-release tests, canary check...

System Design
1
0
11 people solved
May 20, 2026
Figureai logo
Figureai
Medium
Data EngineerSenior+ Locked

Diagnose the Leading Cause of Slow or Failed Charging

Analyze a robot-charging reliability case by defining failure and exposure, ranking root causes, and investigating slow sessions with imperfect teleme...

Analytics & Experimentation
1
0
15 people solved
May 20, 2026
Capital One logo
Capital One
Medium
Data Engineer

Explain production model drop to a PM

Role Play: Communicate Model Degradation to a Non-Technical PM You are a data scientist. A PM says: > “Your model performed great on the validation se...

Behavioral & Leadership
8
0
83 people solved
Mar 1, 2026
Capital One logo
Capital One
Medium
Data Engineer

Answer conflict and ambiguity with STAR stories

Job Fit / Behavioral: STAR Stories Prepare answers using the STAR (Situation, Task, Action, Result) format for the following prompts: 1. Tell me about...

Behavioral & Leadership
16
0
124 people solved
Mar 1, 2026
Amazon logo
Amazon
Easy
Data Engineer

Solve two string problems

You are asked to solve the following two coding problems. 1. Count unique Morse code translations You are given an array of lowercase English w...

Coding & Algorithms
7
0
101 people solved
Jan 24, 2026
Stripe logo
Stripe
Hard
Data Engineer

Compute transaction fees from a CSV string

You are given a CSV string representing a list of payment transactions. Write a function that parses the CSV and returns a CSV string with the total p...

Coding & Algorithms
12
0
228 people solved
Feb 24, 2026
Disney logo
Disney
Medium
Data Engineer

Design a Pipeline to Process a Billion Clickstream Events Per Day

You are designing the data infrastructure for a large consumer web/mobile product. The product emits a clickstream: every page view, tap, scroll, and ...

System Design
4
0
36 people solved
Feb 20, 2026
LinkedIn logo
LinkedIn
Medium
Data EngineerSenior+

Describe a project and its impact

You are interviewing for a Staff Data Engineer role supporting information security (e.g., detection engineering, alerting, network/security infrastru...

Behavioral & Leadership
5
0
50 people solved
Feb 2, 2026
Point72 logo
Point72
Hard
Data Engineer

Implement Portfolio Trading Optimizer

You are given an array prices, where prices[i] is the price of one stock on day i. You may perform multiple buy/sell transactions over the period, but...

Coding & Algorithms
2
0
30 people solved
Apr 30, 2026
Point72 logo
Point72
Easy
Data Engineer Locked

Solve SQL and PySpark Data Tasks

This question evaluates proficiency in SQL query design and PySpark DataFrame manipulation, covering aggregation, filtering, joins, time and numeric f...

Coding & Algorithms
1
0
16 people solved
May 27, 2026
Otter.Ai logo
Otter.Ai
Medium
Data EngineerSenior+

Defend a Data Pipeline Architecture and Its Trade-offs

Prompt Choose one production data pipeline that you personally designed or substantially changed. Walk through its architecture, the requirements that...

System Design
0
0
13 people solved
Apr 2, 2026
Rbcroyalbank logo
Rbcroyalbank
Easy
Data EngineerSenior+

Explain ETL schema changes and ensure integrity

Behavioral / Data Engineering Discussion Prompts You’re interviewing for a Data Engineer role. Answer the following prompts with concrete examples fro...

Behavioral & Leadership
6
0
68 people solved
Feb 8, 2026
Meta logo
Meta
Medium
Data Engineer

Answer DE behavioral and ramp-up questions

Answer the following behavioral questions for a Data Engineer (or data-focused full-stack) role. Provide specific examples. 1. Project under a tight d...

Behavioral & Leadership
5
0
67 people solved
Mar 1, 2026
Amazon logo
Amazon
Medium
Data Engineer

Describe past NLP work and collaboration

Scenario In an initial phone screen, the interviewer asks you to introduce yourself, then drills into your resume. Questions (answer using concrete ex...

Behavioral & Leadership
13
0
113 people solved
Mar 1, 2026
Meta logo
Meta
Medium
Data Engineer Locked

Write queries for follows and bookings

This question evaluates the ability to manipulate temporal event logs, enforce bidirectional relational integrity, and implement efficient graph and i...

Coding & Algorithms
25
1
173 people solved
Mar 1, 2026
Robinhood logo
Robinhood
Hard
Data Engineer

Write SQL to analyze CA residency history

You have two SQL tables. Table 1: user_info (current snapshot) - id (user id) - shard (partition column) - name - country - state - created_date Table...

Software Engineering Fundamentals
9
0
93 people solved
Feb 28, 2026
Bytedance logo
Bytedance
Medium
Data Engineer Locked

Find Top Creators and Login Streaks

This question evaluates proficiency in data aggregation, algorithmic optimization for top-k retrieval, and SQL time-series analysis using window funct...

Coding & Algorithms
3
0
56 people solved
Mar 12, 2026
Apple logo
Apple
Hard
Data Engineer

Encode and Rebuild a Binary Tree

Design two functions for a binary tree: - encode(root) -> string - decode(data) -> root Each tree node contains an integer value and has up to two chi...

Coding & Algorithms
10
0
86 people solved
Apr 2, 2026

Frequently Asked Questions

How difficult are Data Engineer interviews on this 160-question page in 2026?
Across 160 real Data Engineer interview questions the overall difficulty ranges from straightforward SQL and ETL troubleshooting to demanding system-design and reliability problems. Early-stage screens and take-home or online assessments typically emphasize SQL, data-modeling, and short Python or PySpark exercises at an easy-to-medium level; onsite technical rounds shift to medium-to-hard topics such as Spark optimization, streaming semantics, and distributed joins. Senior-level interviews add architecture, capacity planning, and SLA-driven tradeoffs that feel closer to a systems engineering loop. Expect lower tolerance for fuzzy answers and higher emphasis on production-readiness than many pure analytics interviews.
What does a typical Data Engineer interview process look like and where are these roles most common now?
Typical loops begin with a recruiter screen for fit and background, followed by an online assessment or take-home focused on SQL and pipeline coding, then two to four technical interviews and one behavioral or stakeholder round; senior roles add a system-design interview. Data Engineering roles appear across data-platform teams, ads and recommendations, analytics engineering, and finance/trading groups. Companies hiring heavily in 2026 include TikTok, Point72, Disney, and RBC, with recurring themes: TikTok emphasizes Hive/Spark and streaming at recommendation scale; Point72 focuses on low-latency market-data and reliability; Disney often asks about event-driven media pipelines and analytics; RBC centers on cloud data platforms, governance, and robust ETL.
How should I plan my prep timeline and what are realistic stage-by-stage timelines for interviews?
A realistic hiring timeline runs three to six weeks from first contact to offer in many companies. Stage-by-stage: recruiter screen within 1 week, online assessment or homework within 3–7 days, first technical rounds spread over 1–2 weeks, system-design or senior-technical round during week 2–3 if applicable, and a behavioral/stakeholder loop plus final decision in the following week. For preparation, plan a focused four-week plan: week one on core SQL and data-modeling, week two on PySpark/ETL and streaming basics, week three on system design and tradeoffs with a mock interview, and week four on behavioral stories, resume-to-project alignment, and polishing timed exercises.
What key technical subtopics should I master for Data Engineer interviews in 2026?
Master SQL fundamentals including joins, aggregation, window functions, common-table-expressions, and performance patterns such as predicate pushdown and partition pruning. Be fluent in data modeling for OLAP and event schemas, ETL/ELT design, and batch versus streaming tradeoffs including exactly-once semantics and watermarking. Know big-data engines (Spark, Flink), message buses (Kafka), and cloud warehouses or lakehouses (Snowflake, BigQuery, Databricks patterns). Also practice pipeline observability, testing and CI for data jobs, query optimization, and cost/latency tradeoffs; be prepared to reason about metadata, retention, and data governance.
What standout tips will improve my chances and what common pitfalls should I avoid?
Standout tips: bring concrete metrics and outcomes for projects, explain tradeoffs with cost and latency in mind, and walk interviewers through execution plans and failure modes. Use a recent incident or postmortem to show ownership, discuss monitoring and alerting, and demonstrate automated testing for data quality. Common pitfalls include skipping clarifying questions, glossing over late or duplicate data, ignoring observability, over-optimizing without measuring, and failing to quantify impact. Communicate clearly, write readable SQL in interviews, and always justify design choices relative to stakeholders and SLAs.

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