Data Engineer Technical Interview Preparation from Udemy

While some companies consider the Data Engineer role to be a subcategory of the Software Development Engineer role, topics covered in Data Engineer technical interviews differ from those covered in Software Development Engineer interviews. This course focuses on those you will most likely encounter during your Data Engineer interview.
Data Engineering interviews grew by 40% in 2020 and Data Engineering in general is one of the fastest growing job role. These numbers will likely continue to grow as companies invest in data-driven solutions.

What you’ll learn

In this course, you will learn how to prepare for the Data Engineer technical interview at FAANG companies. The lectures will guide you through the concepts you should focus on as a Data Engineer and provide you with practice problems after each topic to test your understanding.
The course will cover the following topics: Problem Sense, Data Modeling, and Coding. The coding practice problem solutions will be written in SQL and Python.
You will practice classic coding problems as well as how to handle batch and streaming data for ETL related interview rounds/questions.
The course will conclude with general tips to remember throughout your interview process.

Are there any course requirements or prerequisites?

There is no prerequisites for this course, however any work experience as a Data Engineer will be helpful.

Who this course is for:

Students and professionals striving to land a Data Engineer position at a FAANG company.

What's inside

Learning objective

How to crack the data engineer technical interview

Syllabus

Understand how to use the course

Title

Introduction

Interview Format

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Focuses on topics commonly encountered in Data Engineer technical interviews, differentiating it from Software Development Engineer interviews

Covers problem sense, data modeling, and coding, which are essential skills for data engineering roles at FAANG companies

Includes practice problems with solutions in SQL and Python, which are widely used languages in data engineering

Teaches how to handle batch and streaming data for ETL-related interview questions, which is a crucial aspect of data engineering

Experience as a Data Engineer will be helpful, suggesting that the course may be more beneficial for those with some prior knowledge

Includes case studies and database design questions, which are relevant to real-world data engineering scenarios

Reviews summary

Prepare for data engineer interviews

Learners interested in technical interview preparation for Data Engineer roles, particularly at FAANG companies, will find this course covers relevant topics. According to the syllabus, the course includes instruction on Problem Sense, Data Modeling, and Coding in SQL and Python, accompanied by practice problems. It also offers general interview tips. While listed as having no prerequisites, prospective students may benefit from some prior technical background in the covered areas to maximize their learning.

Prior experience is beneficial despite no prerequisites.

"Having some background in SQL and Python is really helpful for this course."

"Absolute beginners might find the pacing challenging without prior exposure."

"I could follow along better because I already knew some basics."

Provides useful overall interview guidance.

"The final section with general tips was a nice addition."

"Liked the advice on how to approach different question types."

"Small tips that can make a difference on interview day."

Solid introduction to required technical skills.

"The coverage of SQL and Python fundamentals is a good starting point."

"It helped me review basic coding and data structure concepts."

"I found the explanations clear for someone needing a refresh."

Includes useful practice problems.

"The practice problems after each section helped reinforce my understanding."

"Having problems for SQL and Python was very helpful."

"Working through the case study examples was insightful for that interview type."

Material aligns with technical interviews.

"The content provided feels highly relevant for Data Engineering technical interviews."

"It covers the key areas typically tested in coding and data modeling rounds."

"I feel better prepared for the types of questions asked at major tech companies."

May not be deep enough for experienced candidates.

"As an experienced engineer, I found some sections too basic."

"Could benefit from more advanced topics or deeper dives into system design."

"Good for a quick overview, but not comprehensive for senior roles."

Some content may need updating.

"Interview trends evolve; some material might feel slightly dated."

"Wish there was coverage of newer tools or techniques relevant today."

"Hope the course gets updated periodically to stay current."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Data Engineer Technical Interview Preparation with these activities:

Brush up on SQL Fundamentals

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Reviewing SQL fundamentals will help you tackle the coding problems in the course more effectively.

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Review basic SQL syntax (SELECT, INSERT, UPDATE, DELETE).
Practice writing simple queries on sample datasets.
Familiarize yourself with common SQL functions (e.g., COUNT, AVG, SUM).

Review 'Data Modeling for the Business'

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Reviewing this book will strengthen your understanding of data modeling concepts.

View Cassandra Data Modeling and Schema Design on Amazon

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Read the chapters on conceptual and logical data modeling.
Practice creating data models for different business scenarios.
Compare your models with the examples in the book.

SQL Coding Challenges on LeetCode

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Practicing SQL coding challenges will improve your problem-solving skills and familiarity with common interview questions.

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Solve SQL problems on LeetCode or HackerRank.
Focus on problems related to data manipulation and aggregation.
Analyze the solutions and understand different approaches.

Four other activities

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Mock Interview with a Peer

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Practicing with a peer will help you get comfortable with the interview format and receive valuable feedback.

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Find a peer who is also preparing for data engineering interviews.
Take turns asking each other interview questions.
Provide constructive feedback on each other's answers.

Read 'Designing Data-Intensive Applications'

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Reading this book will provide a deeper understanding of the concepts covered in the course and prepare you for more advanced topics.

View Secret Colors on Amazon

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Read the chapters related to data modeling and distributed systems.
Take notes on key concepts and design patterns.
Reflect on how the concepts apply to real-world data engineering challenges.

Design a Data Warehouse Schema

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Designing a data warehouse schema will reinforce your understanding of data modeling principles and best practices.

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Choose a business domain (e.g., e-commerce, finance).
Identify key business metrics and dimensions.
Design a star schema or snowflake schema to support the metrics.
Document the schema and explain the design choices.

Blog Post: Data Engineering Interview Tips

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Writing a blog post will help you consolidate your knowledge and share your insights with others.

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Brainstorm topics related to data engineering interviews.
Outline the structure of the blog post.
Write the content, focusing on clarity and accuracy.
Edit and proofread the blog post before publishing.

Career center

Learners who complete Data Engineer Technical Interview Preparation will develop knowledge and skills that may be useful to these careers:

Data Engineer

The role of a Data Engineer involves building and maintaining the infrastructure that allows for data analysis and reporting. This course, focused on preparing for Data Engineer technical interviews, helps significantly. Through practical exercises in data modeling, SQL, and Python, you gain experience in the specific skills needed for this role. Crucially, the course addresses coding problems and how to handle batch and streaming data for ETL processes, a key component of a Data Engineer's work. For those looking to secure a Data Engineer position, this course can help you succeed in the interview process.

See salaries and explore the career path for Data Engineer

Cloud Data Engineer

A Cloud Data Engineer designs and implements data solutions in the cloud. While this course centers on the technical interview process for a Data Engineer, the skills you gain, such as problem-solving with SQL and Python, plus the understanding of data modeling will help you succeed in this role. Being able to solve common coding problems in SQL and Python helps with designing and implementing data pipelines. For those who seek a Cloud Data Engineer role, this course is helpful.

See salaries and explore the career path for Cloud Data Engineer

Analytics Engineer

An Analytics Engineer transforms raw data into a format that can be used for analysis. This role requires a deep understanding of data modeling, SQL, and data pipelines, all of which are covered in this course designed for Data Engineer interviews. The problem-solving approach covered here is valuable for designing efficient data systems. The emphasis on batch and streaming data also aligns with the work of an Analytics Engineer, who needs to work with different data sources. For those seeking an Analytics Engineer role, this course can be very useful.

See salaries and explore the career path for Analytics Engineer

Data Architect

The role of a Data Architect involves designing and managing how data is stored, processed, and used within an organization. This course, which provides prep for Data Engineer interviews, can be useful in helping you prepare for the technical skills necessary in this role. The understanding of data modeling is important, and the coverage of how to handle batch and streaming data is relevant for designing robust data architectures. If you wish to become a Data Architect, this course can be useful.

See salaries and explore the career path for Data Architect

ETL Developer

An ETL Developer builds the processes to extract, transform, and load data. This course, though designed for Data Engineers, covers the use of SQL and Python for handling batch an streaming data which are the technologies that an ETL developer will likely use. The syllabus emphasis on problem solving is also beneficial for an ETL Developer, who must be able to design and debug data pipelines. If you are looking to become an ETL developer, this course may help you build solid and relevant skills.

See salaries and explore the career path for ETL Developer

Data Warehousing Engineer

A Data Warehousing Engineer designs, develops, and maintains data warehousing systems. This course can be useful in preparing you for the kind of problems and challenges you will likely encounter. A fundamental aspect of this course is data modeling, which is core to the design of a data warehouse. The course also covers SQL for manipulating data and batch and streaming data. If you seek this role, this course may help you build familiarity with many of the key concepts.

See salaries and explore the career path for Data Warehousing Engineer

Database Administrator

A Database Administrator is responsible for the performance, integrity, and security of databases. This course may be useful in helping you prepare for the technical aspects of your job. Knowledge of Data Modeling, as is in this course's syllabus, allows you to design efficient database schemas, which is important. The practice problems in SQL also help with the manipulation and querying of data in a relational database environment, and the practice with problems related to batch and streaming data can be important. If you are seeking this role, this course provides some relevant foundations.

See salaries and explore the career path for Database Administrator

Machine Learning Engineer

A Machine Learning Engineer focuses on building and deploying machine learning models. While this course is primarily for Data Engineers, it covers areas such as data handling (batch and streaming) and coding with SQL and Python, which are relevant in the early stages of preparing data for machine learning models. The problem-solving and logic skills honed in this course can be useful. If you seek this role, this course may help you strengthen your skills in data handling and coding, which are important early steps in a machine learning pipeline.

See salaries and explore the career path for Machine Learning Engineer

Business Intelligence Developer

A Business Intelligence Developer designs and builds systems that transform raw data into business insights. While this course is geared towards Data Engineers, the SQL skills developed here can be useful for data extraction from databases, critical in a Business Intelligence Developer's work. Also, having a solid understanding of data modeling, which is part of the course, is useful in this role. This course may help strengthen your skills relevant in the Business Intelligence field

See salaries and explore the career path for Business Intelligence Developer

Data Analyst

The Data Analyst interprets data and transforms it into actionable insights. While this course focuses on the technical interview for Data Engineers, some skills, like problem-solving, SQL, and Python, learned here can also be useful. The coverage of data modeling also helps you to understand how data is structured. If you seek a role as a Data Analyst, this course may help build a foundation in various concepts useful in your role.

See salaries and explore the career path for Data Analyst

Research Scientist

A Research Scientist uses data analysis techniques to generate insights and hypotheses. While this course is designed for Data Engineers, the coding skills in SQL and Python are useful in cleaning and preparing data for research. The problem-solving techniques covered in this course are also helpful in data analysis tasks. If you are seeking a role as a Research Scientist, this course may help you develop necessary skills for data analysis.

See salaries and explore the career path for Research Scientist

Data Consultant

A Data Consultant advises organizations on how to improve their use of data. While this course focuses on preparing for Data Engineer technical interviews, the problem-solving skills and understanding of data concepts, such as data modeling, are useful for those in Data Consultant roles. If you wish to be a Data Consultant, this course may help build a solid understanding of core data engineering concepts.

See salaries and explore the career path for Data Consultant

Solutions Architect

A Solutions Architect designs and oversees the implementation of technology solutions. While this course is targeted at Data Engineers, the skills covered, specifically data modeling and system design, are useful for a Solutions Architect. The problems around batch and streaming data also align with the kind of challenges a Solution Architect may encounter. Those who seek to become a Solutions Architect may find that this course helps build a valuable foundation in data management and related systems.

See salaries and explore the career path for Solutions Architect

Software Developer

A Software Developer is responsible for designing, coding, and testing software. While the focus of this course is on Data Engineering, several skills such as problem-solving using code, particularly in Python, can be useful for a Software Developer. The course covers classic coding problems as well as handling batch and streaming data, which may be relevant depending on the project. If you are looking to become a Software Developer, this course may be beneficial as it helps build fundamental coding and problem solving skills.

See salaries and explore the career path for Software Developer

Quantitative Analyst

A Quantitative Analyst, also known as a Quant, uses mathematical and statistical models to help businesses make decisions. While the focus of this course is on preparing for Data Engineer technical interviews, the coding skills using SQL and Python are useful for data processing and analysis. The focus on problems involving lists and dictionaries is also relevant. If you wish to be a Quantitative Analyst, this course may help you build a foundation in technical skills important in a quant role.

See salaries and explore the career path for Quantitative Analyst

Data Engineer Technical Interview Preparation

What's inside

Learning objective

Syllabus

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Reviews summary

Prepare for data engineer interviews

Activities

Career center

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