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Data Engineering Interview Guide

$39

SQL challenges, ETL design questions, data modeling exercises, and system design scenarios specific to data engineering roles.

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📁 File Structure 8 files

data-engineering-interview/ ├── LICENSE ├── README.md ├── questions/ │ ├── big-data-and-tools.md │ ├── data-modeling.md │ ├── data-system-design.md │ ├── etl-pipeline-design.md │ └── sql-challenges.md └── study-plan/ └── data-engineering-study-plan.md

📖 Documentation Preview README excerpt

Data Engineering Interview Guide

110+ technical interview questions with detailed answers covering SQL, ETL pipelines, data modeling, system design, and big data tools — everything you need to land a data engineering role.

Whether you're targeting your first DE position or preparing for a senior/staff interview loop, this guide gives you real questions asked at top companies — with thorough answers, working SQL, ASCII architecture diagrams, and a structured 4-week study plan.

Table of Contents

  • [What's Included](#whats-included)
  • [How to Use This Guide](#how-to-use-this-guide)
  • [File Index](#file-index)
  • [Question Difficulty Breakdown](#question-difficulty-breakdown)
  • [Tips for Interview Day](#tips-for-interview-day)
  • [FAQ](#faq)
  • [Support](#support)
  • [License](#license)

What's Included

CategoryCountDescription
SQL Challenges22Joins, window functions, CTEs, aggregations, query optimization — with worked SQL solutions
ETL Pipeline Design22Batch vs streaming, idempotency, incremental loads, orchestration, backfills, data quality
Data Modeling22Star/snowflake schemas, SCDs, normalization, dimensional modeling, partitioning strategies
Data System Design20End-to-end system design walkthroughs with ASCII architecture diagrams
Big Data & Tools22Spark internals, Kafka, file formats, shuffle optimization, distributed processing tradeoffs
Study Plan14-week structured prep plan with daily topics and practice milestones
Total questions108Every question includes a detailed answer, key points, and optional follow-ups

How to Use This Guide

For Interview Prep (recommended)

1. Start with the study plan (study-plan/data-engineering-study-plan.md) — it maps out a 4-week schedule with daily topics

2. Assess your level — skim each topic file and note which difficulty levels you struggle with

3. Work through weak areas first — if SQL window functions trip you up, drill questions/sql-challenges.md before moving to system design

4. Practice explaining answers out loud — reading the answer is not the same as articulating it under pressure

For Quick Reference

  • Jump directly to any topic via the [File Index](#file-index) below
  • Each file is self-contained — no need to read in order
  • Use Ctrl+F / Cmd+F to search for specific concepts (e.g., "SCD Type 2", "broadcast join", "idempotent")

For Mock Interviews

1. Pick a random file and question number

2. Set a timer: 5 minutes for conceptual questions, 15 minutes for SQL/code, 25 minutes for system design

3. Answer without looking at the guide

4. Compare your answer against the provided answer and key points

5. Note gaps — those are your study priorities

... continues with setup instructions, usage examples, and more.

📄 Content Sample questions/big-data-and-tools.md

Big Data Tools & Distributed Systems

22 interview questions on Spark internals, Kafka, file formats, distributed processing, and storage — with detailed answers, code examples, and real-world performance context.

Questions

Q1. Explain the core components of Apache Spark's architecture. What roles do the driver, executors, tasks, and stages play?

Difficulty: Junior

Answer:

Spark uses a master-worker architecture built around four key abstractions:


┌──────────────────────────────────────────────────────┐
│                   SPARK APPLICATION                  │
│                                                      │
│  ┌────────────┐        ┌──────────────────────────┐  │
│  │   DRIVER   │        │    CLUSTER MANAGER        │  │
│  │            │◄──────►│  (YARN / K8s / Standalone)│  │
│  │ SparkContext│        └──────────────────────────┘  │
│  │ DAG Sched. │                                      │
│  │ Task Sched.│        ┌───────────┐ ┌───────────┐   │
│  └─────┬──────┘        │ EXECUTOR 1│ │ EXECUTOR 2│   │
│        │               │ ┌──┐ ┌──┐│ │ ┌──┐ ┌──┐ │   │
│        └──────────────►│ │T1│ │T2││ │ │T3│ │T4│ │   │
│                        │ └──┘ └──┘│ │ └──┘ └──┘ │   │
│                        │  Cache   │ │  Cache    │   │
│                        └───────────┘ └───────────┘   │
└──────────────────────────────────────────────────────┘

Driver — The JVM process that runs your main() function. It creates the SparkContext, converts your logical plan into a physical DAG of stages and tasks, and coordinates execution across executors. The driver also collects results for actions like collect() or count().

Executors — Worker JVM processes launched on cluster nodes. Each executor runs tasks, stores cached data in memory/disk, and reports status back to the driver. Executors persist for the lifetime of the application.

Stages — The DAG scheduler divides a job into stages at shuffle boundaries. Within a single stage, all transformations can be pipelined (e.g., map → filter → map) without exchanging data between nodes.

Tasks — The smallest unit of work. Each task processes one partition of data within one stage. A stage with 200 partitions produces 200 tasks, distributed across available executor cores.


# Observing stages and tasks in practice
from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("ArchitectureDemo").getOrCreate()

*... and much more in the full download.*
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