Cracking the Data Engineering Interview : Land Your Dream Job with the Help of Resume-Building Tips, over 100 Mock Questions, and a Unique Portfolio / Kedeisha Bryan and Taamir Ransome.

Format:

Book

Author/Creator:

Bryan, Kedeisha, author.

Ransome, Taamir, author.

Language:

English

Subjects (All):

Database management--Problems, exercises, etc.

Database management.

Big data--Problems, exercises, etc.

Big data.

Employment interviewing--Problems, exercises, etc.

Employment interviewing.

Physical Description:

1 online resource (0 pages)

Edition:

First edition.

Place of Publication:

Birmingham, England : Packt Publishing, [2023]

Biography/History:

Bryan Kedeisha: Kedeisha Bryan is a data professional with experience in data analytics, science, and engineering. She has prior experience combining both Six Sigma and analytics to provide data solutions that have impacted policy changes and leadership decisions. She is fluent in tools such as SQL, Python, and Tableau. She is the founder and leader at the Data in Motion Academy, providing personalized skill development, resources, and training at scale to aspiring data professionals across the globe. Her other works include another Packt book in the works and an SQL course for LinkedIn Learning. Ransome Taamir: Taamir Ransome is a Data Scientist and Software Engineer. He has experience in building machine learning and artificial intelligence solutions for the US Army. He is also the founder of the Vet Dev Institute, where he currently provides cloud-based data solutions for clients. He holds a master's degree in Analytics from Western Governors University.

Summary:

Get to grips with the fundamental concepts of data engineering, and solve mock interview questions while building a strong resume and a personal brand to attract the right employers Key Features Develop your own brand, projects, and portfolio with expert help to stand out in the interview round Get a quick refresher on core data engineering topics, such as Python, SQL, ETL, and data modeling Practice with 50 mock questions on SQL, Python, and more to ace the behavioral and technical rounds Purchase of the print or Kindle book includes a free PDF eBook Book Description Preparing for a data engineering interview can often get overwhelming due to the abundance of tools and technologies, leaving you struggling to prioritize which ones to focus on. This hands-on guide provides you with the essential foundational and advanced knowledge needed to simplify your learning journey. The book begins by helping you gain a clear understanding of the nature of data engineering and how it differs from organization to organization. As you progress through the chapters, you'll receive expert advice, practical tips, and real-world insights on everything from creating a resume and cover letter to networking and negotiating your salary. The chapters also offer refresher training on data engineering essentials, including data modeling, database architecture, ETL processes, data warehousing, cloud computing, big data, and machine learning. As you advance, you'll gain a holistic view by exploring continuous integration/continuous development (CI/CD), data security, and privacy. Finally, the book will help you practice case studies, mock interviews, as well as behavioral questions. By the end of this book, you will have a clear understanding of what is required to succeed in an interview for a data engineering role. What you will learn Create maintainable and scalable code for unit testing Understand the fundamental concepts of core data engineering tasks Prepare with over 100 behavioral and technical interview questions Discover data engineer archetypes and how they can help you prepare for the interview Apply the essential concepts of Python and SQL in data engineering Build your personal brand to noticeably stand out as a candidate Who this book is for If you're an aspiring data engineer looking for guidance on how to land, prepare for, and excel in data engineering interviews, this book is for you. Familiarity with the fundamentals of data engineering, such as data modeling, cloud warehouses, programming (python and SQL), building data pipelines, scheduling your workflows (Airflow), and APIs, is a prerequisite.

Contents:

Cover

Title Page

Copyright and Credits

Contributors

Table of Contents

Preface

Part 1: Landing Your First Data Engineering Job

1

Chapter 1: The Roles and Responsibilities of a Data Engineer

Roles and responsibilities of a data engineer

Responsibilities

An overview of the data engineering tech stack

Summary

2

Chapter 2: Must-Have Data Engineering Portfolio Projects

Technical requirements

Must-have skillsets to showcase in your portfolio

Ability to ingest various data sources

Data storage

Data processing

Cloud technology

Portfolio data engineering project

Scenario

3

Chapter 3: Building Your Data Engineering Brand on LinkedIn

Optimizing your LinkedIn profile

Your profile picture

Your banner

Header

Crafting your About Me section

Initial writing exercise

Developing your brand

Posting content

Building your network

Sending cold messages

4

Chapter 4: Preparing for Behavioral Interviews

Identifying six main types of behavioral questions to expect

Assessing cultural fit during an interview

Utilizing the STARR method when answering questions

Example interview question #1

Example interview question #2

Example interview question #3

Example interview question #4

Example interview question #5

Reviewing the most asked interview questions

Part 2: Essentials for Data Engineers Part I

5

Chapter 5: Essential Python for Data Engineers

Must-know foundational Python skills

SKILL 1 - understand Python's basic syntax and data structures

SKILL 2 - understand how to use conditional statements, loops, and functions

SKILL 3 - be familiar with standard built-in functions and modules in Python

SKILL 4 - understand how to work with file I/O in Python.

SKILL 5 - functional programming

Must-know advanced Python skills

SKILL 1 - understand the concepts of OOP and how to apply them in Python

SKILL 2 - know how to work with advanced data structures in Python, such as dictionaries and sets

SKILL 3 - be familiar with Python's built-in data manipulation and analysis libraries, such as NumPy and pandas

SKILL 4 - understand how to work with regular expressions in Python

SKILL 5 - recursion

Technical interview questions

Python interview questions

Data engineering interview questions

General technical concept questions

Chapter 6: Unit Testing

Fundamentals of unit testing

Importance of unit testing

Unit testing frameworks in Python

Process of unit testing

Must-know intermediate unit testing skills

Parameterized tests

Performance and stress testing

Various scenario testing techniques

Unit testing interview questions

Chapter 7: Database Fundamentals

Must-know foundational database concepts

Relational databases

NoSQL databases

OLTP versus OLAP databases

Normalization

Must-know advanced database concepts

Constraints

ACID properties

CAP theorem

Triggers

Chapter 8: Essential SQL for Data Engineers

Must-know foundational SQL concepts

Must-know advanced SQL concepts

Part 3: Essentials for Data Engineers Part II

Chapter 9: Database Design and Optimization

Understanding database design essentials

Indexing

Data partitioning

Performance metrics

Designing for scalability

Mastering data modeling concepts

Chapter 10: Data Processing and ETL

Fundamental concepts

The life cycle of an ETL job.

Practical application of data processing and ETL

Designing an ETL pipeline

Implementing an ETL pipeline

Optimizing an ETL pipeline

Preparing for technical interviews

Chapter 11: Data Pipeline Design for Data Engineers

Data pipeline foundations

Types of data pipelines

Key components of a data pipeline

Steps to design your data pipeline

Chapter 12: Data Warehouses and Data Lakes

Exploring data warehouse essentials for data engineers

Architecture

Schemas

Examining data lake essentials for data engineers

Data lake architecture

Data governance and security

Data security

Part 4: Essentials for Data Engineers Part III

Chapter 13: Essential Tools You Should Know

Understanding cloud technologies

Major cloud providers

Core cloud services for data engineering

Identifying ingestion, processing, and storage tools

Data storage tools

Mastering scheduling tools

Importance of workflow orchestration

Apache Airflow

Chapter 14: Continuous Integration/Continuous Development (CI/CD) for Data Engineers

Understanding essential automation concepts

Test automation

Deployment automation

Monitoring

Mastering Git and version control

Git architecture and workflow

Branching and merging

Collaboration and code reviews

Understanding data quality monitoring

Data quality metrics

Setting up alerts and notifications

Pipeline catch-up and recovery

Implementing CD

Deployment pipelines

Infrastructure as code

Chapter 15: Data Security and Privacy

Understanding data access control

Access levels and permissions

Authentication versus authorization

RBAC

Implementing ACLs.

Mastering anonymization

Masking personal identifiers

Applying encryption methods

Encryption basics

SSL and TLS

Foundations of maintenance and system updates

Regular updates and version control

Chapter 16: Additional Interview Questions

Index

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Notes:

Includes index.

Description based on print version record.

ISBN:

9781837631070

1837631077

OCLC:

1407093814