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
Meta logo
Meta
Medium
Data Engineer

Return count and renewal percentage of unreturned good copies

Tables: copies(copy_id, condition), checkouts(copy_id, checkout_date, return_date, renewal_count). Write a single SQL query that returns one row with ...

Data Manipulation (SQL/Python)
0
0
4 people solved
Sep 6, 2025
Meta logo
Meta
Medium
Data Engineer

Compute missing letters to form original string

Implement a function that, given two strings original and typed (typed is a misspelled/partial version of original), returns the number of additional ...

Coding & Algorithms
8
1
62 people solved
Sep 6, 2025
Meta logo
Meta
Medium
Data Engineer

Validate alternating checkout/return logs

Given a chronological list of events logs of the form (timestamp, book_id, is_checkout) where is_checkout is True for a checkout and False for a retur...

Coding & Algorithms
8
0
59 people solved
Sep 6, 2025
Meta logo
Meta
Medium
Data Engineer

Compute reservation diff for largest member

Given copies(copy_id, reserved_by_member_id) and members(member_id, referred_by_member_id), find the member with the largest member_id. Return a singl...

Data Manipulation (SQL/Python)
1
0
4 people solved
Sep 6, 2025
Bloomberg logo
Bloomberg
Medium
Data Engineer

Write Postgres string parsing and aggregation query

You are given a PostgreSQL table events(user_id TEXT, raw TEXT) where raw stores pipe-delimited key=value pairs, for example 'user=U1|country=US|ts=20...

Data Manipulation (SQL/Python)
0
0
2 people solved
Sep 6, 2025
Meta logo
Meta
Medium
Data Engineer

Recommend friends-of-friends

Question Given a dictionary such as {A:[B,C], B:[C,D], C:[E]}, return for a user U all people followed by U’s followees but not already followed by U....

Coding & Algorithms
3
1
65 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Engineer

Count active follow connections

Question Write SQL to return the current number of active follow connections. Events table columns: user_id, target_id, event_type ('request_follow', ...

Data Manipulation (SQL/Python)
0
1
7 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Engineer

Query carpool ride metrics

Question For a ride-sharing product with carpool capability, answer a series of SQL questions (e.g., daily completed pooled rides, average seats utili...

Data Manipulation (SQL/Python)
1
2
7 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Engineer

Compute cumulative metrics with full joins

Tables: - daily_metrics(date DATE, content_id STRING, daily_value BIGINT) - cumulative_metrics(date DATE, content_id STRING, cumulative_value BIGINT) ...

Data Manipulation (SQL/Python)
0
0
8 people solved
Aug 1, 2025
Meta logo
Meta
Medium
Data Engineer

Return top-3 content per category

Given a collection of items with fields (content_id, category, rating), implement top_k_by_category(items, k= 3) that returns, for each category, the ...

Data Manipulation (SQL/Python)
1
0
3 people solved
Aug 1, 2025
Meta logo
Meta
Medium
Data Engineer

Recommend two-hop follows in Python

Given a directed "follows" graph as a Python dict[str, list[str]], implement recommend_two_hop(graph, user) that returns the set (or a sorted list) of...

Data Manipulation (SQL/Python)
3
0
31 people solved
Aug 1, 2025
Meta logo
Meta
Medium
Data Engineer

Check carpool trip feasibility

You are given a list of trips where each trip i is (passengers_i, start_i, end_i) with start_i < end_i on a one-dimensional route. A single vehicle wi...

Coding & Algorithms
6
0
77 people solved
Aug 1, 2025
Affirm logo
Affirm
Medium
Data Engineer

Write SQL for Transactions and Customers

You are given two tables: - customers(customer_id INT PRIMARY KEY, signup_date DATE, region VARCHAR, segment VARCHAR) - transactions(transaction_id IN...

Data Manipulation (SQL/Python)
0
0
6 people solved
Aug 1, 2025
Meta logo
Meta
Medium
Data Engineer

Design visualizations for streaming metrics

Design visualizations for streaming metrics Design a Monitoring and Diagnosis Visualization for a Video-Streaming Metric Context You are building an o...

Analytics & Experimentation
6
0
47 people solved
Aug 1, 2025
Meta logo
Meta
Medium
Data Engineer

Write SQL for library analytics

Given a library database, write SQL to answer the following: 1) Count the number of books that are currently not returned (i.e., still checked out) an...

Data Manipulation (SQL/Python)
1
0
14 people solved
Jul 16, 2025
Meta logo
Meta
Medium
Data Engineer

Solve library coding tasks in Python

Solve library coding tasks in Python Implement the following Python tasks: 1) Given a list of (category, points) for books, choose up to 3 books with ...

Coding & Algorithms
12
0
86 people solved
Jul 16, 2025
TikTok logo
TikTok
Medium
Data Engineer

Debug a Hive insert query

Given a Hive table schema and an incoming table plus an INSERT/SELECT statement meant to inject data, identify why the query fails and provide step-by...

Data Manipulation (SQL/Python)
0
0
5 people solved
Jul 15, 2025
Meta logo
Meta
Medium
Data Engineer

Write SQL and Python for data prep

Given clickstream events (user_id, event_type, ts, properties) and a users table (user_id, signup_ts, plan), write SQL to compute DAU/WAU/MAU, D1/W1 r...

Data Manipulation (SQL/Python)
1
1
8 people solved
Jul 15, 2025
TikTok logo
TikTok
Medium
Data Engineer

Explain a SQL query result

Given two tables and a specific SQL query, precisely explain the expected result set: which rows are returned, what each column contains, how joins/fi...

Data Manipulation (SQL/Python)
1
0
10 people solved
Jul 15, 2025
Google logo
Google
Medium
Data EngineerIntern

Count Special Index Pairs

Given an integer array nums of length n, count the number of index pairs (i, j) such that: - 0 <= i < j < n - nums[i] * nums[j] is even - j - i is odd...

Coding & Algorithms
2
0
39 people solved
Jan 26, 2025

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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