Data engineering is a fascinating and fulfilling career — you are at the helm of every business operation that requires data, and as long as users generate data, businesses will always need data engineers. In other words, job security is guaranteed.
But, with such great power comes great responsibility. The journey to becoming a successful data engineer features tricky terrain that you need to navigate and get right from the start. In this short and to-the-point article, I’ll walk you through the entire process of becoming a data engineer, helping you dodge the common pitfalls.
What is Data Engineering?
Data Engineering refers to creating practical designs for systems that can extract, keep, and inspect data at a large scale. It involves building pipelines that can fetch data from the source, transform it into a usable form, and analyze variables present in the data. These pipelines draw hidden insights about a business’s overall functioning and help stakeholders understand their customers, outreach, sales, etc.
Why do companies hire a Data Engineer?
In 2021, Gartner predicted that 85%of the data-based projects would fail and deliver the desired results. But, with companies gradually raising their investments in data infrastructures, the prediction is likely to turn out to be false. Along with that, the companies are likely to hire experts who can help them leverage data efficiently. And that is why the business managers look for data engineers, as they are the ones who will interact with the raw data, clean it, polish it, and make it analysis-ready.
Data Engineer: Job Growth in Future
The demand for data engineers has been on a sharp rise since 2016. Years after that, we find a shortage in the number of skilled data engineers and an increase in the number of jobs. As per a 2021 report by DICE, data engineer is the fastest-growing job role and witnessed 50% annual growth in 2022.
What are the Roles and Responsibilities of Data Engineer?
- Convert erroneous data into a usable form for further analysis.
- Create large data warehouses using ETL.
- Develop, test, and maintain architectures.
- Develop dataset processes.
- Deploy Machine Learning and statistical methods.
Skills Required In Data Engineer
Here is a list of skills needed to become a data engineer:
- Highly skilled at graduation-level mathematics.
- Good skills in computer programming languages like R, Python, Java, C++, etc.
- High efficiency in advanced probability and statistics.
- Ability to demonstrate expertise in database management systems.
- Experience with using cloud services providing platforms like AWS/GCP/Azure.
- Good knowledge of various machine learning and deep learning algorithms will be a bonus.
- Knowledge of popular big data tools like Apache Spark, Apache Hadoop, etc.
- Good communication skills as a data engineer directly works with the different teams.
8 Steps to Becoming a Data Engineer:
To succeed in this career path, I’ve mentioned that you’ll need a specific set of skills. Here are seven steps that will help you acquire them.
1. Build your Foundation
There are so many intricacies of becoming a Data Engineer, and it can become a bit overwhelming at times. But the only thing that will keep you grounded on the roadmap is building a solid foundation.
To become a Data Engineer, you should have a good understanding of Programming languages and Software Engineering concepts. The industry standard mainly revolves around two technologies: Python and SQL.
Start with Python and after having a good understanding of Python, learn the basics of SQL. You can learn these languages with these resources-
Resources:
If you chose Python as your programming language, here are some recommended courses:
Python
- Programming for Everybody (Getting Started with Python) — (Coursera )(University of Michigan)
- Introduction to Python Programming- (Udacity Free Course)
- Python 3 Tutorial — (SOLOLEARN)
- CS DOJO — (YouTube)
- Programming with Mosh — (YouTube)
- Corey Schafer — (YouTube)
2. Get In-Depth Knowledge of SQL and NoSQL
Start with learning SQL. SQL is the most demanding skill for Data Engineer. That’s why you should have a strong understanding of SQL. Knowledge of NoSQL is also required because sometimes you have to deal with unstructured data.
You can learn SQL and NoSQL from these below courses.
- SQL for Data Analysis — (Udacity)
- SQL for Data Science — (Coursera)
- Intro to Relational Databases — (Udacity)
- Introduction to Structured Query Language SQL — (Coursera)
- Intro to SQL — (Kaggle)
3. Learn Data Integration and ETL Pipelines
Data integration is the process of combining data from different sources and consolidating it into a single, unified view. Data integration is critical for modern data engineering, as organizations often have data stored in disparate systems that must be combined to gain a comprehensive view of the data.
ETL (Extract, Transform, Load) is a commonly used approach to data integration. In ETL, data is first extracted from source systems, then transformed into a format that is compatible with the target system, and finally loaded into the target system. ETL is a batch process that typically runs on a scheduled basis, such as nightly or weekly.
- Understanding of data integration techniques and best practices
- Experience with ETL tools such as Apache NiFi, Apache Kafka, and Talend
- Familiarity with data quality and data profiling tools to ensure the accuracy of the data being integrated.
Here are some resources for learning these tools.
Resources
- INFORMATICA TUTORIAL — (Guru99)
4. Learn Big Data Tools
The next step in the Data Engineering roadmap is to learn big data tools. Below are all the big data tools you should learn for data engineering:
- Apache Hadoop
- Apache Spark
- Apache Kafka
- Apache Airflow
- MongoDB
You should have at least basic knowledge of all these tools. You can learn Big Data from these courses-
Resources
- Intro to Hadoop and MapReduce — (Udacity)
- Spark (Udacity)
- Big Data Specialization (Coursera)
5. Learn Cloud Computing
Cloud computing platforms like Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure provide a range of services for storing, processing, and analyzing data. These platforms offer a variety of benefits for data engineers, including scalable infrastructure, on-demand computing resources, and a range of tools for data processing and analysis.
Apart from this knowledge of DevOps principles and CI/CD pipelines would be an added advantage.
More and more application workloads are moving to the different cloud platforms. That’s why the data science/engineering community must have a good understanding of these clouds.
You can learn Cloud Computing with these courses-
Resources
- Data Engineering, Big Data, and Machine Learning on GCP Specialisation (Coursera)
- Intro to Cloud Computing (FREE Course)
- Become an AWS Cloud Architect
6. Learn Machine Learning and Data Visualisation
As a Data Engineer, it’s not compulsory to have Machine Learning knowledge, but having a basic knowledge of ML Algorithms is a plus for you. You can learn Machine Learning Basics with the “Machine Learning by Andrew Ng” FREE Course.
You should have a basic understanding of Data Visualisation tools. You can learn either Tableau or PowerBI.
7. Do Some Projects
It seems like that’s a lot of learning — it is. That’s why it is imperative that you feel proficient in each of those areas to be a successful Data Engineer. You can do this stage during your learning or after — it is up to you. Some people prefer to apply their knowledge and skill after all the learning, some prefer to do it during, in order to test themselves.
So the next stage is applying your code and putting your skills to the test.
Ideas for Data Engineering projects
- Data Engineering Zoomcamp
- Scrape Stock and Twitter Data Using Python, Kafka, and Spark
- Web-scraping with real-estates
- Building A Data Platform
- Snowflake Real-Time Data Warehouse
Out of Data Engineering, you can practice your coding skills with LeetCode challenges, however, this can be applied to the majority of tech careers.
8. Develop your communication skills
Last but not least, data engineers also need communication skills to work across departments and understand the needs of data analysts and data scientists as well as business leaders. Depending on the organisation, data engineers may also need to know how to develop dashboards, reports, and other visualisations to communicate with stakeholders.
9. Now Take your First Step as Data Engineer
Now you have all the data engineering skills and projects, it’s time to take your first step as Data Engineer. And that is Make a Strong Resume.
Your Resume is the first impression for any recruiters. No matter how skilled you are, if your resume is not attractive, sorry you will not get an interview call. That’s why you shouldn’t ignore your Resume.
Wrapping It Up
Data engineering is arguably one of the fastest-growing positions in the technology sector, thanks to the rise of big data and data science applications.
And with the increasing demand, today, data engineering is a lucrative career. According to Glassdoor, the average data engineer in the U.S. earns over $110,000 per year. And an experienced data engineer working for a giant tech company can earn as much as $150,000 per year.
Leverage this guide to start your career in data engineering and set yourself up for success!
Hope you found this Article helpful!
Happy Learning !!
Let me know through the comments your review!
Follow Arif Alam For More.
