The Ultimate Data Engineering Interview Guide

Introduction

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:

• The must-know concepts—because you can’t pass an interview if you don’t understand the basics.
• SQL and Python coding questions—I’ll show you the kinds of problems you’ll face and how to solve them.
• System design challenges—this is where companies test if you can actually build large-scale data pipelines.
• Take-home assignments and case studies—these are common, so I’ll help you get ready.
• Behavioral questions—yep, they matter too. You need to show you’re a great team player.

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!

1. Core Data Engineering interview Concepts You Must Know

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:

Data Modeling & Databases

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.

Relational Databases (SQL-based)

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.

• Examples: PostgreSQL, MySQL, SQL Server
• Used for: Banking systems, customer management, business applications

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

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:

1. Key-Value Stores: Simple key and value pairs, used for fast lookups and caching. Examples: DynamoDB, Redis
2. Document Stores: Store semi-structured data, like JSON. Good for storing flexible data. Examples: MongoDB, CouchDB
3. Wide Column Stores: Store large amounts of data across many servers. Good for handling billions of records. Examples: Cassandra, HBase
4. Graph Databases: Used for data with complex relationships, like social networks. Examples: Neo4j

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.

Data Warehousing Solutions

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.

• Examples: Snowflake, Amazon Redshift, Google BigQuery
• Used for: Business reports, data analytics, finding trends

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.

Common Interview Questions

Interviewers may ask:

• What’s the difference between SQL and NoSQL?
• When would you use DynamoDB instead of MySQL?
• How would you design a database for an online store with millions of users?
• Where would you store terabytes of data for business analysis?

If you know which database to use for which problem, you will be ready for any question.

Data Lake vs. Data Warehouse: What’s the Difference?

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.

What is a Data Lake?

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.

• Examples: Amazon S3, Azure Data Lake
• Used for: Data science, machine learning, storing logs

When should you use it?

• When you have a lot of data but don’t know how you’ll use it yet.
• If you’re working on machine learning or big data analytics you can use it.
• When you need to store huge amounts of logs or raw files.

What is a Data Warehouse?

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.

• Examples: Snowflake, Amazon Redshift
• Used for: Business intelligence, reports, dashboards

When should you use it?

• When you need fast analysis and structured reports
• If your company wants to track sales, user behavior, or trends you can use it
• When you need a system that works well with SQL queries

So, which one should you choose?

• If you need to store everything and process it later, go with a data lake.
• If you need fast insights and organized data, go with a data warehouse.

That’s it! Simple, right?

ETL Pipelines & Data Processing

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.

ETL vs. ELT – When to Use Each

• ETL (Extract → Transform → Load): First, we pull data from sources (Extract). Then, we clean and process it (Transform) before saving it in a database (Load).
  • Best for: Traditional data warehouses where structured data is needed.
  • Example: A bank processes customer transactions before storing them in a database.
• ELT (Extract → Load → Transform): Here, we first Extract the raw data and Load it into a data lake. Then, we Transform it inside the database when needed.
  • Best for: Big data and cloud-based analytics.
  • Example: A company loads raw logs into Snowflake and processes them later.

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.

Batch Processing

Some data jobs run in batches—for example, processing sales reports once a day. These batch jobs are handled by frameworks like:

• Apache Spark: Used for fast processing of large datasets.
• Apache Airflow: Great for scheduling and managing ETL workflows.
• Luigi: A Python-based tool for managing batch jobs.

If a company updates customer analytics every night, batch processing is the right choice.

Streaming Processing

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:

• Apache Kafka: A messaging system that helps move real-time data between services.
• Apache Flink: A powerful tool for processing real-time streams.
• Spark Streaming: Allows Spark to process live data streams.

A ride-sharing app like Uber needs streaming processing to track drivers and passengers in real time.

Data Ingestion

Before processing, we need to collect data from different sources. This step is called data ingestion, and the right tool depends on your needs:

• Kafka: Best for high-volume, real-time messaging.
• AWS Kinesis: A cloud-based service for streaming data.
• Google Pub/Sub: A messaging system for real-time applications.

If you want to process millions of events per second, tools like Kafka or Kinesis are the best choice.

What You’ll Be Asked in Interviews

Expect questions like:

• What’s the difference between ETL and ELT?
• When would you use batch vs. streaming processing?
• How would you design a data pipeline for a real-time dashboard?
• How does Kafka help with data ingestion?

Understanding how data moves from one place to another is key to being a great data engineer.

Big Data Technologies

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.

Hadoop: The Grandfather of Big Data

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:

1. Store massive data across multiple computers.
2. Process that data in parallel using MapReduce.

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:

• HDFS (Hadoop Distributed File System) – Think of it like Google Drive but spread across many computers. It stores huge files and makes copies so nothing is lost if a machine crashes.
• MapReduce – A method for processing data in batches. It splits the work across multiple machines, does calculations, and then combines the results.

💡 Example:
Imagine you need to count how many times each word appears in millions of tweets.

• Hadoop splits the data into chunks and sends each chunk to a different computer.
• Each computer counts words in its chunk.
• Hadoop combines the counts to get the final result.

👉 Problem? Hadoop is slow because it writes data to disk after every step. That’s why we needed something faster.

Apache Spark: The Faster Alternative

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:

• Hadoop is like a notebook where you write things down after every step.
• Spark is like mental math, where you keep numbers in your head and only write down the final answer.

With Spark, you can:

• Process data faster because it doesn’t write to disk each time.
• Handle both batch and real-time data.
• Use Python (PySpark) instead of Java, making it easier to work with.

💡 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.

• Hadoop would process this hours later.
• Spark detects it immediately and blocks the transaction.

Modern Storage Solutions: Delta Lake, Iceberg, and Hudi

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:

• Slow performance – Traditional data lakes take too long to query.
• Data inconsistency – If two people update data at the same time, results might be wrong.
• No easy updates – If you want to update a single row, you have to rewrite the whole file.

These new tools solve those issues:

💡 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.

Common Interview Question: Apache Spark vs. Hadoop’s MapReduce

Question:
How does Apache Spark handle big data differently from MapReduce?

Think about it like this:

• Hadoop writes everything to disk after each step → slow
• Spark keeps data in memory → much faster
• Hadoop is batch-only (good for log analysis)
• Spark does both batch and streaming (good for fraud detection, real-time analytics)
• Spark is easier to use (supports Python)

Final Thoughts

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.

Cloud & DevOps for Data Engineering

Step 1: Why Do Companies Use the Cloud Instead of Physical Servers?

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.

Option 1: Buying Your Own Servers (Old Way)

💾 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:

1. Expensive – You have to buy new servers whenever your data grows.
2. Hard to Maintain – If a server breaks, you need IT experts to fix it.
3. Not Flexible – If your website suddenly gets a million visitors, your server might crash.

Option 2: Using Cloud Services (New Way)

☁️ Instead of buying servers, companies now rent them from cloud providers like Amazon, Google, and Microsoft.

✅ Benefits of Cloud:

1. Cheaper – You pay only for what you use.
2. Scalable – If your data grows, you can increase storage instantly.
3. Reliable – If one server fails, another one takes over automatically.

💡 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.

Step 2: What Do Cloud Providers Offer?

Different companies provide cloud services, but the three biggest ones are:

• AWS (Amazon Web Services)
• GCP (Google Cloud Platform)
• Azure (Microsoft Cloud)

Each provider gives you tools to store, process, and analyze data. Let’s look at some key tools they offer.

💡 Think of AWS, GCP, and Azure like three different fast-food chains.

• One sell burgers (data storage).
• Another one sell fries (data processing).
• They just cook them in slightly different ways.

Step 3: Why Do We Use Docker & Kubernetes?

Problem: Running Software on Different Computers

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.

Solution: Docker & Kubernetes

✔ 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:

• They package the program in Docker (so it works anywhere).
• They deploy it using Kubernetes, which automatically scales it up if more sales data comes in.

Step 4: How to Design a Data Pipeline in AWS? (Interview Example)

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.

1. Ingest Data (Getting Data Into the Cloud)

How do we get data into the system?

• If data is coming in real-time (like website traffic), use AWS Kinesis.
• If data is coming in batches (like daily reports), use AWS Lambda to trigger uploads.

💡 Example:
Imagine you own a sports website. Every time someone clicks on an article, the data is sent to AWS Kinesis in real-time.

2. Transform Data (Cleaning & Processing)

How do we process the data?

• If the data is small, use AWS Glue (serverless ETL).
• If the data is big, use Apache Spark on AWS EMR (for large-scale transformations).

💡 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.

3. Store Data (Saving It for Future Use)

💾 Where do we save the processed data?

• Store raw data in Amazon S3 (cheap storage).
• Store structured data in Amazon Redshift (optimized for analytics).

💡 Example:
After cleaning your sports website data, you store it in Redshift to analyze which articles are most popular.

4. Orchestrate the Workflow (Automating Everything)

How do we automate the pipeline?

• Use Apache Airflow (if you want more control).
• Use AWS Step Functions (if you want an easier, managed solution).

💡 Example:
Every night at midnight, your system automatically pulls data, cleans it, and stores it without any human effort.

Final Recap (Let’s Sum It Up in Simple Words)

• Cloud computing replaces expensive physical servers.
• AWS, GCP, and Azure provide different tools to store, process, and analyze data.
• Docker makes applications work on any computer.
• Kubernetes automatically manages and scales applications.
• A data pipeline moves data, cleans it, and saves it for analysis.

Must Read

• How to Write a Python Program to Find LCM
• Complete Guide to Find GCD in Python: 7 Easy Methods for Beginners
• How to Set Up CI/CD for Your Python Projects Using Jenkins
• The Ultimate Guide to the range() Function in Python
• How to compute factorial in Python

SQL and Python Interview Questions (With Solutions)

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 Interview Questions

SQL is a must-have skill for working with databases. You need to know how to filter, aggregate, join tables, and optimize queries.

1. Find the Second Highest Salary

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

Solution:

SELECT DISTINCT salary 
FROM employees 
ORDER BY salary DESC 
LIMIT 1 OFFSET 1;

How it works:

• ORDER BY salary DESC → Sorts salaries from highest to lowest.
• DISTINCT salary → Removes duplicates (important if two employees have the same salary).
• LIMIT 1 OFFSET 1 → Skips the first result (highest salary) and picks the second highest.

2. Find Employees With the Highest Salary in Each Department

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:

• We join the employees table with departments.
• The subquery finds the maximum salary per department.
• The WHERE condition ensures only employees with the highest salary in their department are selected.

Python Interview Questions

Python is important for data transformation, automation, and pipeline development. You’ll be asked to process large datasets, optimize code, and write efficient algorithms.

1. Remove Duplicates From a List

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:

• We use a set to track seen numbers.
• If a number is already in the set, it is skipped.
• This preserves the order of the list.

2. Find Missing Number in a List

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:

• The sum of the first N natural numbers is N × (N+1) / 2.
• We subtract the sum of the given list from the expected sum.

What to Expect in Interviews

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.

System Design Challenges in Data Engineering

When an interviewer asks, “How would you design a data pipeline for a streaming application?”, they’re testing:

• How you structure data ingestion, processing, and storage
• Which technologies you choose and why
• How you handle failures, scalability, and performance

Let’s go over a real-world example and break it down.

Scenario: Tracking User Clicks in Real-Time

Imagine you work for a company that tracks user clicks on a website. Every time a user clicks on something, we want to:

1. Collect the data immediately.
2. Process it in real-time (filter, clean, and count clicks per user).
3. Store the processed data so analysts can run reports.
4. Visualize the data in dashboards.

Step 1: Data Ingestion (Collecting Click Data)

The Problem:

We need a system that can handle millions of clicks per second coming from users worldwide.

The Best Tool:

Kafka is the most popular choice.
Other options: AWS Kinesis (Amazon) and Google Pub/Sub (Google Cloud).

Why Kafka?

• Can handle millions of events per second without slowing down.
• It stores the click events so they don’t get lost (durability).
• It works well with real-time processing tools like Apache Spark and Flink.

How It Works:

1. When a user clicks on something, the website sends a click event (data) to Kafka.
2. Kafka stores these click events in a queue called a topic.
3. The next step is to process this data in real-time.

Step 2: Data Processing (Cleaning and Aggregating Data)

The Problem:

Now, we have millions of raw clicks. But raw data is messy:

• Some clicks might be duplicates or errors.
• We need to count how many times each user clicked within a second.
• The data needs to be organized before storing it.

The Best Tool:

Apache Spark Streaming (most common in interviews).
Other option: Apache Flink (lower latency but less common).

Why Spark Streaming?

• It processes data in small batches, which makes it faster and scalable.
• It integrates well with Kafka to read incoming data.
• Many companies already use Spark, so it’s a safe interview answer.

How It Works:

1. Spark Streaming reads click data from Kafka.
2. It filters out invalid clicks (e.g., missing data, errors).
3. It groups clicks per user, per second (aggregation).
4. The clean and processed data is sent to storage.

Step 3: Data Storage (Saving the Processed Data)

The Problem:

We now have clean, structured data. But where do we store it?

• If we want to run analytics, we need a fast database.
• If we just want to store raw data, we need cheap storage.

The Best Tool:

BigQuery (Google Cloud) or Snowflake (best for analytics).
Other options: Cassandra (for fast lookups), S3 (for raw storage).

Why BigQuery or Snowflake?

• They are optimized for big data queries.
• It allow us to run SQL queries on billions of records.
• They automatically scale to handle more data.

How It Works:

1. Spark Streaming sends the processed data to BigQuery/Snowflake.
2. The data is stored in tables, making it easy to run queries.
3. Analysts can now use this data for reports and insights.

Step 4: Data Visualization (Making Reports & Dashboards)

The Problem:

Analysts want to see the data in charts and dashboards instead of raw numbers.

The Best Tool:

Looker (Google) or Tableau (most common for dashboards).
Other options: Power BI, Presto, Trino.

Why Looker or Tableau?

• They create beautiful charts and dashboards from BigQuery/Snowflake data.
• This allow non-technical users to explore the data easily.
• They are widely used in companies.

How It Works:

1. The stored data in BigQuery/Snowflake is connected to Looker/Tableau.
2. Analysts can create charts and reports on user engagement trends.
3. The company can now see how users interact with the website in real time.

Final Architecture (Step-by-Step Flow)

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.

What This Achieves:

• Handles millions of clicks per second.
• Scales easily as traffic grows.
• Ensures no data is lost (fault-tolerant).
• Provides real-time insights into user behavior.

Common Follow-Up Interview Questions

1. How do you handle failures?

• Kafka Replication → Copies data across multiple servers to prevent data loss.
• Spark Checkpoints → Saves progress so it can restart from the last good point if it crashes.

2. How do you scale this pipeline?

• Increase Kafka partitions → More partitions mean more messages can be processed in parallel.
• Autoscale Spark → Spark can automatically add more computing power when needed.
• Store old data in S3 → Move old data to cheaper storage to save money.

Why Is This Important?

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.

Take-Home Assignments & Case Studies in Data Engineering

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.

What Are Interviewers Looking For?

They want to see if you can:

• Write clean and well-documented code
• Build efficient and scalable pipelines
• Use SQL and Python properly
• Explain trade-offs (why you chose a certain tool or approach)

Example Take-Home Assignment

Scenario:

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!

Step 1: Ingesting Data (Reading Raw Data)

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?

• Pandas → If the dataset is small
• Apache Airflow → When you need to schedule and automate the process
• AWS S3 + Spark → If the dataset is huge

For this example, let’s use Pandas (since it’s easier for small datasets).

Python Example: Read CSV Using Pandas

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.

Step 2: Cleaning the Data

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

Python Example: Cleaning Data in Pandas

# 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!

Step 3: Load Data into a Database

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

Python Example: Storing Data in PostgreSQL

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!

Step 4: Automate the Process with Apache Airflow

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

Python Example: Airflow DAG (Data Pipeline)

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?

• We define a DAG (order_etl) that runs daily.
• We create a task (extract_transform_load) that runs our ETL script.
• Airflow will automate the process every day.

Now, the pipeline runs automatically!

Common Case Study Questions in Interviews

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?”

Key Talking Points:

1. Optimize SQL Queries

• Use indexes to speed up searches
• Avoid SELECT * (only query the columns you need)
• Optimize JOINs (use efficient keys)

2. Partition Large Tables

• Split big tables into smaller partitions for faster queries

3. Use Caching

• Store frequent queries in Redis or Memcached

4. Scale Compute Resources

• Increase Spark executors for faster processing
• Use parallel processing to handle more data

If you can explain these optimizations, you’ll impress the interviewer!

Why This Matters?

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.

Behavioral Interview Questions

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:

• If you handle challenges. Then how will you do it.
• How you explain your ideas
• How you work in a team

Let’s break it down.

1. Common Behavioral Questions

Interviewers like to ask about real situations to see how you approach problems. Here are some typical questions you might hear:

• “Tell me about a time you fixed a broken data pipeline.”
• “Describe a time you optimized a slow SQL query. What steps did you take?”
• “How do you handle disagreements with team members?”
• “What’s the biggest mistake you’ve made as a data engineer? How did you fix it?”
• “Tell me about a time you had to learn a new technology quickly.”

They don’t just want what you did—they want to know how you think through problems.

2. How to Answer (STAR Method)

A great way to answer is the STAR method:

• Situation – What was the problem?
• Task – What was your role?
• Action – What steps did you take?
• Result – What happened in the end?

3. Example Answer (Fixing a Broken Pipeline)

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:

• Re-ran the job with a backup file
• Added error handling to retry API calls
• Set up alerts in Airflow to catch failures earlier

Result – The pipeline was back up in less than an hour. I also helped the team improve monitoring to prevent future failures.

4. Final Tips for Behavioral Interviews

• Practice out loud – Don’t just think about your answers—say them.
• Be honest – If you haven’t faced a situation, explain how you would handle it.
• Show teamwork – Many questions test how well you work with others.

Now You’re Ready!
If you prepare technical answers and explain your thinking clearly, you’ll stand out in any data engineering interview.

Conclusion: Ace Your Data Engineering Interview with Confidence

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:

• Technical Questions: Master SQL, Python, ETL processes, and system design to showcase your problem-solving skills.
• Behavioral Questions: Use the STAR method to structure your answers and highlight your experience.
• Case Studies & Take-Home Assignments: Approach real-world problems logically, document your thought process, and optimize for efficiency.
• Final Interview Tips: Practice out loud, stay calm under pressure, and show not just your skills but also how you think.

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!

FAQs on Data Engineering Interview Preparation

External Resources

Hands-on Coding Practice

1. StrataScratch – Real-world SQL interview questions from top tech companies
2. Mode Analytics SQL Tutorial – Great for learning advanced SQL concepts
3. Data Engineering Interview Questions (Interview Query) – Curated questions from real interviews

Tools & Platforms to Get Hands-on Experience

1. Apache Airflow – Learn how to build and orchestrate ETL pipelines
2. Google Cloud BigQuery Sandbox – Practice querying large datasets for free
3. Kaggle Datasets – Work on real-world datasets to build projects

By exploring these resources, you’ll gain the technical knowledge and practical experience needed to ace your data engineering interview.

About The Author

Emmimal Alexander

Emmimal Alexander is an AI & Machine Learning Expert, passionate educator, and the author of “Neural Networks and Deep Learning with Python.” As the founder of EmiTechLogic, she’s on a mission to make complex tech topics accessible, engaging, and empowering for learners at every level.

With deep expertise in Python, HTML, JavaScript, and CSS, Emmimal brings a strong coding foundation to her tutorials and educational resources. Her work focuses on blending theoretical understanding with real-world application—so readers not only learn how things work, but also why they matter.

Through EmiTechLogic, she creates hands-on guides, detailed breakdowns, and project-based learning content that bridges the gap between academic concepts and practical implementation. Whether you’re exploring AI for the first time or fine-tuning your neural networks, you’re in the right place.

See author's posts