Ashish is a techology consultant with 13+ years of experience and specializes in Data Science, the Python ecosystem and Django, DevOps and automation. He specializes in the design and delivery of key, impactful programs.
For enquiries call:
+1-469-442-0620
- Home
- Blog
- Data Science
- Data Engineering Learning Path: A Complete Roadmap
HomeBlogData ScienceData Engineering Learning Path: A Complete Roadmap
Share
Data Engineering Learning Path: A Complete Roadmap
Published
07th Sep, 2023
Views
Read TimeRead it in
7 Mins
In this article
Data engineers make a tangible difference with their presence in top-notch industries, especially in assisting data scientists in machine learning and deep learning. The data engineer learning path includes having set-skills and awareness of the process and channel data and having the zest to work as a frontline technician who can retrieve data from various data sources.
Before you get into the stream of data engineering, you should be thorough with the skills required, market and industry demands, and the role and responsibilities of a data engineer. Let us understand here the complete big data engineer roadmap to lead a successful Data Engineering Learning Path.
Career Learning Path for Data Engineer
You must have the right problem-solving and programming data engineer skills to establish a successful and rewarding Big Data Engineer learning path. The core objective is to provide scalable solutions to data analysts, data scientists, and decision-makers of organizations. Data engineering is one of the highest in-demand jobs in the technology industry and is a well-paying career.
The average salary in the US is $131,610, and the range is from $85,604 to $202,340, according to Indeed (May 2023). You should have appropriate data engineer skills, expertise, educational qualification, and experience working as a data engineer. Go for the best courses for Data Engineering and polish your big data engineer skills to take up the following responsibilities:
You should have a systematic approach to creating and working on various data architectures necessary for storing, processing, and analyzing large amounts of data.
You should have the expertise to collect data, conduct research, create models, and identify patterns.
You should be able to establish tasks where manual intervention is replaced by automation.
You should be well-versed with SQL Server, Oracle DB, MySQL, Excel, or any other data storing or processing software.
You must develop predictive models to help industries and businesses make data-driven decisions.
Steps to Become a Data Engineer
One excellent point is that you don’t need to enter the industry as a data engineer. You can start as a software engineer, business intelligence analyst, data architect, solutions architect, or machine learning engineer. You can simultaneously work on your skills, knowledge, and experience and launch your career in data engineering. Let us look at the steps to becoming a data engineer:
Step 1 - Skills for Data Engineer to be Mastered for Project Management
Learn the fundamentals of coding skills, database design, and cloud computing to start your career in data engineering.
Soft Skills
You should have the right verbal and written communication skills required for a data engineer.
You should display a strong work ethic by taking accountability for assigned tasks, meeting deadlines, and ensuring that the project is error-free
Adaptability to change and breaking projects into smaller steps is a great way to start your data engineering career.
Critical thinking and business acumen skills can help you view problems from all sides and acquire comfort in reading financial statements. Maximize your time by focusing on business initiatives and paying attention to stakeholders' challenges.
Collaboration data engineer skills to work well with others and to exhibit an open mind about ideas presented by others. You should be able to work outside your comfort zone and take on projects.
Hard Skills
SQL, which includes memorizing a query and resolving optimized queries.
Data warehousing to aggregate unstructured data collected from multiple sources.
Data architecture to tackle datasets and the relationship between processes and applications.
Coding helps you link your database and work with all programming languages. You should be well-versed in Python and R, which are beneficial in various data-related operations.
Operating system know-how which includes UNIX, Linux, Solaris, and Windows.
Apache Hadoop-based analytics to compute distributed processing and storage against datasets.
Machine learning will link your work with data scientists, assisting them with statistical analysis and modeling.
Other Competencies
You should have proficiency in coding languages like SQL, NoSQL, Python, Java, R, and Scala.
You should be thorough with technicalities related to relational and non-relational databases, Data security, ETL (extract, transform, and load) systems, Data storage, automation and scripting, big data tools, and machine learning.
Equip yourself with the experience and know-how of Hadoop, Spark, and Kafka, and get some hands-on experience in AWS data engineer skills, Azure, or Google Cloud Platform.
Step 2 - Determine Your Ideal Timeline
Your Data Engineer learning path depends on your pace of learning and acquiring the right experience. The ideal timeline and data engineer career path is:
Start as an Entry-level Data Engineer – It is recommended to earn your bachelor’s degree and work on data engineering projects under the guidance of more experienced data engineers.
Move on to the job role of a Junior Data Engineer – at this stage, you should have enough knowledge about programming languages, databases, and big data technologies. You should be able to work on complex projects and design and implement data solutions.
The next stage is to work as a Senior Data Engineer – After you gain expertise in multiple programming languages, databases, and big data technologies, you should be able to work on complex data solutions.
Lead Data Engineer – This is when you can manage a team of data engineers.
Data Architect - You should have the experience and skill to design and implement data architectures that support an organization's business goals.
Data Infrastructure Manager – You should have the skill to organize a massive data infrastructure, oversee data engineers' teams, and work closely with other IT departments.
Chief Data Officer: You should be able to manage an organization's data strategy and oversee data engineering, data analytics, and data governance functions.
Step 3 - How to Choose Project Management Courses for Data Engineer Learning Path?
When choosing project management courses to develop your Data Engineer learning path, you can go for two options:
Pathway 1: Data Engineer Without a Qualification
Although it is highly recommended to get your bachelor’s degree to start your data engineering career, it’s not necessary to have a degree to become a data engineer. If you have a background in data science, computer science, information systems, Software Engineering, Math, or a business-related field, you can simply enroll yourself in Project Management Courses to become a data engineer. Go for IBM Certified Data Engineer, Data Scientist online course, AWS Certified Big Data, Azure Data Engineer Associate, or Google Cloud Certified Professional Data Engineer.
Pathway 2: How to Become a Certified Data Engineer?
You need relevant certifications and practical enterprise-grade projects in data engineering. These certifications will also hone the right skills for data engineering. The best way to become a certified data engineer is through experiential learning, the practice of learning by doing.
Build a strong portfolio that exhibits data engineering projects you've completed independently or as part of coursework. You can also post your work on your LinkedIn profile.
Creating NoSQL Database with MongoDB and Compass or Database Design with SQL Server Management Studio (SSMS)
You should have the expertise to enter Database Creation and Modeling using MYSQL Workbench.
You can highlight your portfolio with standard certifications like Google Data Analytics, IBM Data Science, or IBM Data Engineering Professional Certificates.
Get certified in relational and non-relational database designs, which will help you with proficiency in SQL and NoSQL domains. Experiment with different kinds of projects during your course and participate in open-source projects to polish your skills.
Step 4 - Who Can Become a Data Engineer?
If you have the right skill, knowledge, expertise, certification, data engineering qualifications, and experience, anyone can become a data engineer. Getting a formal degree is necessary; however, getting one is recommended to uplift your profile.
What Degree is Needed to Become a Data Engineer?
First, gaining your bachelor's degree in computer science, statistics, software engineering, mathematics, or a related field is recommended. A master's degree in data science or a related field can give you an edge over other data engineers in salary packages. By earning your degree, you can establish a foundation of the right expertise and knowledge in this evolving field.
Data Engineer Requirements
Before starting your career as a data engineer, you must have some experience working on projects. You can participate in online competitions, try freelancing for some time, or work on hackathons to prove that you know the system to your potential employers. You should be thorough with SQL and NoSQL databases and be skilled in database management, data warehousing, and data modeling.
Connect with other professionals, join communities, learn about industry trends and technologies, and update your knowledge daily. You can start as a software administrator, a database analyst, or a business intelligence analyst. As you progress, you can learn new skills and move on to a full-fledged Data Engineering Career.
Step 5 - What to Study to Become a Data Engineer?
Here is what you must study to become a successful Data engineer:
Best Books
Some of the highly recommended books that you must read are:
The Master Book on Distributed Systems: Designing Data-Intensive Application
Gain Knowledge on Cloud Infra: Kubernetes in Action
The SQL Fundamental Book: T-SQL Querying (Developer Reference)
Understand Data Science: R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
OLAP/Data Warehouse Must Read: The Data Warehouse Toolkit (The Definitive Guide to Dimensional Modeling, 3rd Edition)
In-depth on Python: Fluent Python: Clear, Concise, and Effective Programming 2nd Edition
Preparation Tips
Here are some preparation tips to become a data engineer:
Develop your data engineering skills and polish your fundamentals of database design, Power BI developer key skills, cloud computing, and coding skills
It is highly recommended to get certified, which will not only hone your skills, experience, and knowledge but will also give you a better salary package than a data engineer who isn’t certified.
Build a portfolio of data engineering projects and work on freelancing projects or online competitions before entering the core.
Start with an entry-level position and then move on to the Data Engineer learning path of a successful career.
Interview Questions
To crack that difficult interview, which can be straightforward, if you are well-prepared, you should be thorough with the below-mentioned questions:
What is Data Engineering?
What is Data Modeling?
What are the features of Hadoop?
What is a NameNode?
What are the design schemas available in data modeling?
What is HDFS?
Which frameworks and applications are essential for data engineers?
What are the four Vs. of Big Data?
What are the repercussions of the NameNode crash?
What is the difference between a data engineer and a data scientist?
What is a block and block scanner in HDFS?
What is COSHH?
Explain indexing.
What are the differences between structured and unstructured data?
Explain MapReduce in Hadoop.
What’s the Demand for Data Engineers?
There is a massive increase in demand for data engineering jobs. As discussed earlier, if industries and organizations use data, there will always be a burgeoning demand for data scientists and engineers. Per the Dice 2020 Tech Job Report, there has been a 50% growth in demand for data engineers in India. In 2023, data engineering is one of the most lucrative career options, and related services are expected to cross USD 77.37 billion by the end of 2023. This industry will grow at an impressive CAGR of 17.60%.
Top Data Engineering Certifications
Some of the best Data Engineering certifications are as follows:
Professional Certificate Program in Data Engineering
Data Engineering with AWS Machine Learning
Data Engineering Career Path courses
Microsoft Azure Data Engineering Associate Specialization Course for Azure data engineer learning path
Conclusion
The bottom line is that if you are good at simplifying data and have it in you to make it more reliable and valid for data scientists, then data engineering is for you. As a data engineer, you allow industries and businesses to leverage the value of data analytics, cloud computing, and coding.
Data engineering is not limited in scope, as it incorporates data acquisition, management, infrastructure, mining, processing, and modeling. The objective is to identify and evaluate how data insights affect the business using internal technology, business teams, collaborative partners, and customers.
Frequently Asked Questions (FAQs)
1. What should I study for data engineering?
You must get your degree or major in computer science, software engineering, data science, or information systems for data engineering.
2. Is coding required for data engineers?
Yes! Proficiency in coding languages and programming languages such as SQL, NoSQL, Python, Java, R, and Scala is a must for data engineers.
3. Can a non-IT person become a data engineer?
You can become a data engineer if you have an educational background in computer science, software or computer engineering, applied math, physics, statistics, or a related field. Moreover, even if you don’t have experience working on IT, you can brush up and learn data engineer technical skills with certifications, courses, internships, and real-world experience.
Upcoming Data Science Batches & Dates
| Name | Date | Fee | Know more |
|---|
Course Advisor
Hello! Ready to elevate your career? Explore 500+ future-ready courses with KnowledgeHut upGrad 🚀x
1
Fast forward your career with 500+ future-ready courses ⏩💻👩🎓📚x
1
