Preparing for a data engineering interview—key topics and essential skills at a glance.
Alright, let’s talk about data engineering interview. They can be tough, but don’t worry—I’ll walk you through everything you need to know.
These days, data engineering is a big deal. Companies need solid data pipelines to handle massive amounts of information, make smart decisions, and train machine learning models. That’s why interviews for these roles are so competitive. If you want to stand out, you need to be comfortable with SQL, Python, ETL processes, big data tools, and system design.
So, how do you prepare? Here’s what we’re going to cover:
Whether you’re new to data engineering or trying to land a job at a top tech company, I’ve got you covered. Stick with me, and you’ll walk into your interview ready to crush it. Let’s get started!
Before we get into coding problems or system design questions, let’s talk about the basics. You won’t just be asked to write code in a data engineering interview. Interviewers want to see if you truly understand how data flows through a system, how it’s stored, and how it’s processed at scale. If you skip this part, even simple coding problems can become tricky.
So, what should you focus on? Here are the key areas you need to know:
Let’s talk about how to store and organize data. As a data engineer, this is one of the most important things you need to know. If data is not stored properly, everything else—searching, processing, and analyzing—will be slow or even fail.
The first big question in any project is: What type of database should we use? Different databases have different jobs. Let’s go through them one by one.
These databases store data in tables (like an Excel sheet). They keep everything structured and consistent. They are best for applications where accuracy and order matter, like banking and customer records.
Relational databases use SQL (Structured Query Language) to search and manage data. They work well for small to medium-sized applications. But if your data grows too fast, they can become slow. That’s why we have to use NoSQL.
NoSQL databases are more flexible. They do not use tables like relational databases. Instead, they store data in different ways to handle big data and real-time applications. There are four main types:
If you need fast and scalable storage, NoSQL is often the better choice. For example, a real-time messaging app would use MongoDB or DynamoDB, not MySQL, because these can handle huge amounts of data at high speed.
Regular databases are good for running applications, but what if you need to analyze years of data? That’s where data warehouses come in. They are built for fast analytics on huge amounts of historical data.
A company like Netflix stores years of user data in a warehouse. They use it to find which movies are popular or what users like to watch. These systems scan billions of records in seconds to get insights.
Interviewers may ask:
If you know which database to use for which problem, you will be ready for any question.
Both store data, but they have different jobs. If you pick the wrong one, your system might become slow or expensive. So, let’s break it down.
A data lake is like a giant storage box where you can dump any type of data—structured, unstructured, raw, or processed. It doesn’t care about format or organization. You can store everything now and decide what to do with it later.
When should you use it?
A data warehouse is more like a well-organized library. It only keeps cleaned and structured data, making it easy to run fast queries and create reports. Businesses use data warehouses to make quick decisions based on past data.
When should you use it?
That’s it! Simple, right?
ETL & Data Pipelines
Data doesn’t just sit in one place—it needs to be moved, cleaned, and transformed before it’s useful. That’s where ETL (Extract, Transform, Load) and data pipelines come in. These processes help collect data from different sources, clean it, and store it in a format that’s ready for use.
Now, there’s also ELT (Extract, Load, Transform), which is a bit different. Let’s go over when to use each.
If your data is small and structured, ETL is better. If you’re dealing with huge datasets and need flexibility, ELT is the way to go.
Some data jobs run in batches—for example, processing sales reports once a day. These batch jobs are handled by frameworks like:
If a company updates customer analytics every night, batch processing is the right choice.
Sometimes, you can’t wait for batch jobs. If data is flowing in real-time, like stock prices or social media feeds, you need streaming processing. Some popular tools are:
A ride-sharing app like Uber needs streaming processing to track drivers and passengers in real time.
Before processing, we need to collect data from different sources. This step is called data ingestion, and the right tool depends on your needs:
If you want to process millions of events per second, tools like Kafka or Kinesis are the best choice.
Expect questions like:
Understanding how data moves from one place to another is key to being a great data engineer.
Big data is all about handling huge amounts of information. Regular databases like MySQL or PostgreSQL work fine for smaller datasets, but what happens when companies have terabytes or even petabytes of data?
They need special tools to store, process, and analyze this massive data efficiently. That’s where technologies like Hadoop, Spark, Delta Lake, Iceberg, and Hudi come in. Let’s break these down in a way that makes sense.
Before modern big data tools, companies had a problem: Where do we store all this data? And how do we process it efficiently?
Hadoop was one of the first solutions. It lets you:
Let’s say you work for a social media company that tracks user activity. Every second, thousands of people are liking, sharing, and commenting on posts. You need to store this data safely and then analyze it later to see trends.
Hadoop has two main parts:
💡 Example:
Imagine you need to count how many times each word appears in millions of tweets.
👉 Problem? Hadoop is slow because it writes data to disk after every step. That’s why we needed something faster.
Hadoop was great, but companies wanted something quicker and more flexible. That’s when Apache Spark came along. It’s 10-100x faster than Hadoop because it processes data in memory instead of writing it to disk after every step.
Think of it this way:
With Spark, you can:
💡 Example:
A bank wants to detect fraud in real time. If a customer’s credit card is used in two different countries within minutes, it could be fraud.
Big data storage has also improved. Traditional data lakes (like storing raw files in HDFS) became slow and messy.
That’s where Delta Lake, Apache Iceberg, and Apache Hudi come in. They fix problems like:
These new tools solve those issues:
| Technology | What It’s Best For |
|---|---|
| Delta Lake | Makes Spark-based data lakes more reliable (ensures data consistency). |
| Apache Iceberg | Organizes big data in a structured, efficient way. |
| Apache Hudi | Lets you update or delete records easily without rewriting everything. |
💡 Example:
Imagine a company tracks millions of IoT sensor readings every day. Instead of reprocessing the entire dataset, they use Hudi to update only the new sensor readings, saving time and computing power.
Question:
How does Apache Spark handle big data differently from MapReduce?
Think about it like this:
You don’t need to memorize everything, but understanding when to use each tool is key. If you’re asked:
“When should I use Hadoop?” → When you need reliable batch processing but don’t care about speed.
“Should I use Spark?” → Ofcourse, you need real-time processing or faster batch jobs.
“You may ask when should I use Delta Lake, Iceberg, or Hudi?” → Whenever you need efficient, scalable storage for big data.
Let’s say you run a business. Every day, you collect tons of data—customer orders, website visits, sales reports, etc. You need a place to store this data and a way to analyze it quickly.
💾 Before cloud computing, businesses had to buy their own servers (big, expensive machines). These servers stored all the data and handled all the processing.
🔴 Problems with Physical Servers:
☁️ Instead of buying servers, companies now rent them from cloud providers like Amazon, Google, and Microsoft.
✅ Benefits of Cloud:
💡 Example:
Let’s say you open an online store. At first, you only have 10 customers a day, so you don’t need a huge server. But during Black Friday, suddenly 100,000 customers visit your store. If you use the cloud, it can automatically increase your computing power for that day and then go back to normal afterward.
Different companies provide cloud services, but the three biggest ones are:
Each provider gives you tools to store, process, and analyze data. Let’s look at some key tools they offer.
| Cloud Provider | Tools for Data Engineers | What It Does |
|---|---|---|
| AWS | ✅ S3 | Stores raw data (like a Google Drive for companies) |
| ✅ Glue | Cleans and processes data | |
| ✅ Redshift | A fast database for analytics | |
| ✅ Lambda | Runs small tasks automatically | |
| ✅ Kinesis | Handles real-time data (like live stock prices) | |
| GCP | ✅ BigQuery | A super-fast tool for analyzing big data |
| ✅ Dataflow | Transforms and processes data | |
| ✅ Pub/Sub | Moves data between services in real time | |
| Azure | ✅ Data Factory | Moves data between different tools |
| ✅ Synapse Analytics | Stores and analyzes structured data | |
| ✅ Event Hub | Handles real-time streaming data |
💡 Think of AWS, GCP, and Azure like three different fast-food chains.
Let’s say you create a data processing application that runs on your laptop. But when you send it to your friend’s computer, it doesn’t work because their setup is different.
🔴 This is a common problem in big companies. When they run a program on different cloud servers, it may behave differently.
✔ Docker – Packages everything into one container (like a lunchbox) so the program runs the same way on every machine.
✔ Kubernetes – Manages multiple containers, making sure they run smoothly and scale automatically.
💡 Example:
A company processes daily sales data. Instead of installing the program on every machine:
Imagine someone asks you in an interview:
💡 “How would you design a data pipeline using AWS?”
A data pipeline is just a way to move, clean, and store data so it can be analyzed. Let’s break it into 4 simple steps.
How do we get data into the system?
💡 Example:
Imagine you own a sports website. Every time someone clicks on an article, the data is sent to AWS Kinesis in real-time.
How do we process the data?
💡 Example:
Your sports website collects raw data (timestamps, page views). But you need to clean it (remove duplicates, fix errors). AWS Glue can handle this for you.
💾 Where do we save the processed data?
💡 Example:
After cleaning your sports website data, you store it in Redshift to analyze which articles are most popular.
How do we automate the pipeline?
💡 Example:
Every night at midnight, your system automatically pulls data, cleans it, and stores it without any human effort.
If you’re preparing for a data engineering interview, expect to write code—a lot of it! Interviewers want to see how well you query data, optimize performance, and build data pipelines using SQL and Python.
We’ll go over two key areas:
SQL Questions – Querying and analyzing structured data
Python Questions – Data manipulation and automation
SQL is a must-have skill for working with databases. You need to know how to filter, aggregate, join tables, and optimize queries.
This is a common SQL challenge that tests your ability to work with ranking functions.
Question:
Write a SQL query to find the second highest salary from an employees table.
Table: employees
| id | name | salary |
|---|---|---|
| 1 | John | 5000 |
| 2 | Alice | 7000 |
| 3 | Bob | 6000 |
| 4 | Eve | 7000 |
Solution:
SELECT DISTINCT salary
FROM employees
ORDER BY salary DESC
LIMIT 1 OFFSET 1;
How it works:
Question:
Find the highest-paid employee in each department.
Tables:
employees (id, name, salary, department_id)departments (id, department_name)Solution:
SELECT e.name, e.salary, d.department_name
FROM employees e
JOIN departments d ON e.department_id = d.id
WHERE e.salary = (
SELECT MAX(salary)
FROM employees
WHERE department_id = e.department_id
);
How it works:
employees table with departments.WHERE condition ensures only employees with the highest salary in their department are selected.Python is important for data transformation, automation, and pipeline development. You’ll be asked to process large datasets, optimize code, and write efficient algorithms.
Question:
Write a function to remove duplicates from a list while keeping the order.
Example:
nums = [1, 3, 2, 3, 4, 1, 5]
remove_duplicates(nums) # Output: [1, 3, 2, 4, 5]
Solution:
def remove_duplicates(lst):
seen = set()
return [x for x in lst if not (x in seen or seen.add(x))]
nums = [1, 3, 2, 3, 4, 1, 5]
print(remove_duplicates(nums)) # Output: [1, 3, 2, 4, 5]
How it works:
Question:
Given a list of numbers from 1 to N with one missing number, find the missing number.
Example:
nums = [1, 2, 4, 5, 6]
find_missing_number(nums) # Output: 3
Solution:
def find_missing_number(nums):
n = len(nums) + 1
total_sum = n * (n + 1) // 2
return total_sum - sum(nums)
nums = [1, 2, 4, 5, 6]
print(find_missing_number(nums)) # Output: 3
How it works:
Here are common interview topics for SQL and Python:
✅ SQL: Joins, subqueries, window functions, indexing, query optimization
✅ Python: Data manipulation (Pandas), working with large files, list comprehensions, algorithms
Knowing how to write clean and efficient code will help you stand out in your interview.
Next, let’s go over System Design Challenges and how to handle real-world data pipeline scenarios.
This is the a crucial part of data engineering interviews.
You’ll be asked to design scalable and efficient data pipelines that handle large datasets and support real-time processing.
When an interviewer asks, “How would you design a data pipeline for a streaming application?”, they’re testing:
Let’s go over a real-world example and break it down.
Imagine you work for a company that tracks user clicks on a website. Every time a user clicks on something, we want to:
We need a system that can handle millions of clicks per second coming from users worldwide.
Kafka is the most popular choice.
Other options: AWS Kinesis (Amazon) and Google Pub/Sub (Google Cloud).
Now, we have millions of raw clicks. But raw data is messy:
Apache Spark Streaming (most common in interviews).
Other option: Apache Flink (lower latency but less common).
We now have clean, structured data. But where do we store it?
BigQuery (Google Cloud) or Snowflake (best for analytics).
Other options: Cassandra (for fast lookups), S3 (for raw storage).
Analysts want to see the data in charts and dashboards instead of raw numbers.
Looker (Google) or Tableau (most common for dashboards).
Other options: Power BI, Presto, Trino.
Here’s the complete data pipeline:
1. Kafka → Captures real-time user clicks.
2. Spark Streaming → Processes and aggregates the data.
3. BigQuery/Snowflake → Stores the processed data for analytics.
4. Looker/Tableau → Creates dashboards for analysts.
This is a real-world problem that big tech companies face every day.
Interviewers don’t expect a perfect answer.
They want to see how you think about trade-offs and choose the right tools.
If you understand these steps, you’re well-prepared for any data engineering system design interview.
When you’re interviewing for a data engineering job, companies might give you a take-home assignment or a case study. This helps them see how you build data pipelines in real-world situations.
They want to see if you can:
You’re a data engineer at an e-commerce company. Your job is to build a data pipeline that:
1. Reads customer orders from a CSV file
2. Cleans the data (removes duplicates, fixes errors)
3. Loads the cleaned data into a database
Let’s go step by step!
The company gives you a CSV file with raw order data. Your pipeline needs to read this data and prepare it for processing.
Which tools can you use?
For this example, let’s use Pandas (since it’s easier for small datasets).
import pandas as pd
# Read CSV into a DataFrame
df = pd.read_csv("orders.csv")
# Show first few rows
print(df.head())
What happens here?
pd.read_csv("orders.csv") loads the file into a DataFrame (a table-like structure in Pandas).print(df.head()) shows the first few rows of the dataset.The raw data may have issues like:
1. Duplicate orders (same order appearing twice)
2. Missing values (empty prices, missing dates)
3. Wrong timestamps (incorrect date formats)
How do we clean it?
1. Remove duplicate orders
2. Fill missing values
3. Convert timestamps to correct format
# Drop duplicate rows
df = df.drop_duplicates()
# Fill missing values with defaults
df["price"] = df["price"].fillna(0)
# Convert order_date to proper date format
df["order_date"] = pd.to_datetime(df["order_date"])
# Show data types to confirm changes
print(df.dtypes)
What happens here?
drop_duplicates() removes any duplicate rows.fillna(0) replaces missing prices with 0 (so there are no empty values).pd.to_datetime(df["order_date"]) converts dates into the correct format.Now, the data is cleaned and ready for storage!
After cleaning, we need to store the data in a database (like PostgreSQL or MySQL).
How do we insert data into PostgreSQL?
1. Use SQLAlchemy to connect Python with PostgreSQL
2. Use batch inserts to make it efficient
from sqlalchemy import create_engine
# Connect to PostgreSQL database
engine = create_engine("postgresql://user:password@localhost:5432/orders_db")
# Write DataFrame to SQL database
df.to_sql("orders", con=engine, if_exists="replace", index=False)
What happens here?
create_engine(...) connects Python to the PostgreSQL database.to_sql(...) writes the DataFrame into a table called "orders".Now, the data is saved in the database!
Instead of running this script manually, we can schedule it to run daily using Apache Airflow.
Why use Airflow?
1. Automates ETL workflows (Extract, Transform, Load)
2. Tracks dependencies between tasks
3. Works well for large data pipelines
from airflow import DAG
from airflow.operators.python_operator import PythonOperator
from datetime import datetime
# Define DAG (workflow)
dag = DAG("order_etl", schedule_interval="@daily", start_date=datetime(2024, 3, 1))
# Define task (function to run)
def extract_transform_load():
df = pd.read_csv("orders.csv").drop_duplicates()
df["price"] = df["price"].fillna(0)
df["order_date"] = pd.to_datetime(df["order_date"])
df.to_sql("orders", con=engine, if_exists="replace", index=False)
etl_task = PythonOperator(
task_id="run_etl",
python_callable=extract_transform_load,
dag=dag,
)
etl_task
What happens here?
order_etl) that runs daily.extract_transform_load) that runs our ETL script.Now, the pipeline runs automatically!
Instead of a take-home project, some interviews ask case study questions to test your problem-solving skills.
💡 Example Question:
📌 “Your company’s data pipeline is running slowly. How would you improve performance?”
SELECT * (only query the columns you need)2. Partition Large Tables
3. Use Caching
4. Scale Compute Resources
If you can explain these optimizations, you’ll impress the interviewer!
Take-home assignments and case studies test real-world skills. If you can:
1. Write clean and efficient code
2. Automate your data pipeline
3. Explain trade-offs in tools and techniques
You’ll stand out in data engineering interviews!
Next, let’s cover Behavioral Interview Questions—because technical skills alone won’t get you the job.
Technical skills are important, but they’re not everything. Companies also want to know how you think, how you work with others, and how you solve problems. That’s what the behavioral interview is all about.
Expect questions that test:
Let’s break it down.
Interviewers like to ask about real situations to see how you approach problems. Here are some typical questions you might hear:
They don’t just want what you did—they want to know how you think through problems.
A great way to answer is the STAR method:
Let’s say they ask:
“Tell me about a time you fixed a broken data pipeline.”
You can answer like this:
Situation – At my last job, our daily ETL pipeline failed one morning. No customer orders were updated, which was a big issue for the business.
Task – As the data engineer, I needed to find the issue and fix it before business hours.
Action – I checked the logs and found that a file was missing. After some digging, I realized an API call had failed in the data ingestion step. To fix it, I:
Result – The pipeline was back up in less than an hour. I also helped the team improve monitoring to prevent future failures.
Now You’re Ready!
If you prepare technical answers and explain your thinking clearly, you’ll stand out in any data engineering interview.
Preparing for a data engineering interview might seem overwhelming, but with the right strategy and practice, you can walk in with confidence.
Here’s a quick recap of what we covered:
Landing a data engineering job isn’t just about answering questions correctly—it’s about demonstrating that you can build, optimize, and maintain reliable data systems while working effectively with a team.
Keep practicing, refine your answers, and showcase your passion for data engineering. With preparation and confidence, you’ll be ready to impress your interviewers and take the next step in your career!
By exploring these resources, you’ll gain the technical knowledge and practical experience needed to ace your data engineering interview.
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