Data Engineering is one of the most crucial components of any data-driven organization. The demand for data-driven decision-making is increasing so the data engineering field is becoming really important. All this has led to a rapid increase in demand for data engineers. It is one of the most lucrative career options that promises insanely high salaries, global recognition, immense opportunities, and a lot more. As per the latest report data engineering and big data, services are expected to cross USD 77.37 by the end of 2023. The industry is expected to grow at an amazing rate of CAGR of 17.60%.
There are a lot of skills, technologies, and tools that a data engineer needs to master. In this blog, we will discuss how to become a data engineer. We will provide you with a complete guide to all the important parameters of data engineering. We will also cover who is a data engineer and why to become one. So let’s get started.
Who is a Data Engineer?
Data engineers are responsible for building systems that manage, collect, and transform data into useful information for business analysts to interpret. Fields like machine learning and deep learning require data engineers to process and channel the data. Their main aim is to gain insights from the data so that organizations can use it to evaluate and optimize their performance.
Some of the major responsibilities of a data engineer are:
- Create algorithms to develop data into useful information
- Develop, test, and maintain the database pipeline
- Interact with the data scientist team and provide datasets as per the business requirements
- Create accurate and informative data-driven solutions
- Ensure compliance with security policies and data governance
So till now, you must have got a basic understanding of who is a data engineer and their major responsibilities. Now let’s move to the next section and understand some perks of becoming a data engineer.
Salary of a Data Engineer
The salary of a data engineer can vary significantly based on several factors, including experience, location, education, and the specific industry. Based on the recent stats on average, in India, the salary of a Data Engineer ranges around 6.5 Lakhs/yr to 8.5 Lakhs/yr and in the United States, the salary can range from around $80,000 to $160,000 or more per year. Data engineers with extensive experience, advanced degrees, and specific skills, such as expertise in big data technologies or cloud platforms, can command higher salaries. Also, as the importance of data-driven insights continues to rise, data engineers are likely to remain in high demand and may have competitive compensation packages.
Why Become a Data Engineer?
A career in the data engineering field is both challenging and rewarding. As a data engineer, you will play a crucial role in determining the organization’s success by applying problem-solving skills to create highly scalable solutions. Also, you provide data that data analysts, scientists, and other decision-makers need to do their jobs effectively. Some of the important reasons why you must consider building a career in data engineering are:
- Building Block for Data Science: Data engineers are the backbone of effective data strategy operations. They are the shoulders on which data analysts and data scientists stand.
- Lucrative Career Opportunities: Data engineering is one of the most promising career options. As per the latest survey, a huge jump of 40% is witnessed in data engineering jobs. Also, it is a high-paying career option, as per Glassdoor an average data engineer earns around $115176 to $168000 per annum.
How to Become a Data Engineer?
Are you excited to step into this amazing industry of data engineering? So in this section, we will discuss the most in-demand skills that you need to become a data engineer.
1. Knowledge of Programming
Coding is one of the mandatory skills for data engineers. A basic understanding of programming language is essential for data engineering roles. There are many programming languages that can be used for data engineering but Python is one of the best options. It is the perfect option for writing data pipelines and executing ETL jobs. Another reason to use Python for data engineering tasks is the presence of extensive libraries. These libraries provide a wide range of tools to manipulate, transform and store data effectively. Some of the popular Python libraries that streamline data engineering tasks are as mentioned below:
- Pandas: It is one of the most versatile Python libraries and is frequently used for data manipulation and analysis. Further, it is used to clean, preprocess and transform raw data for analysis.
- TensorFlow: It is one of the popular Python libraries for Artificial intelligence, Machine learning, and deep learning. It has the ability to handle data-related tasks like data transformation, data processing, data visualization, and data analytics.
- Scikit-learn: It is another important Python library that is used for data engineering tasks like regression, classification, and clustering to streamline the building of machine learning models.
- Apache Airflow: It is a powerful Python library used by data engineers to write, schedule and monitor workflow. It helps data engineers to schedule multiple tasks and handle complex data processing.
- Beautiful Soup: It is a Python package for parsing HTML and XML documents. It is one of the most effective tools for web scrapping making it a highly important asset for data engineering.
For more information about how to learn programming visit – How to Learn Programming?
2. Structured Query Language
To become a data engineer it is really crucial to have the knowledge of SQL(Structured Query Language). It is one of the most dominant languages for data operations. Learning SQL helps data engineers easily collaborate with data scientists and analysts as they can have a common language for querying. SQL skills can be applied to a wide range of data management tools making it an important skill for people working in a variety of businesses.
Organizations will be able to gain better insights and make smart decisions if they can integrate data from various resources using SQL. Structured Query Language plays a crucial role in data integration, extraction, and transformation. All this makes it a really important skill for data engineers to learn in order to get a promising career in this industry.
3. Mathematics and Statistics
Data engineers must have good knowledge of mathematical and statistical concepts. They are the building block of all machine learning algorithms. The advanced mathematics concepts that data engineers need to be good with are differential equations, linear algebra, and vector calculus. Also, data engineers have to deal with huge datasets so it is essential to have knowledge of statistical parameters like mean, mode, and median to summarize the data.
4. Cloud Fundamentals
The knowledge of cloud concepts is really important to ace the field of data engineering. Some of the major cloud computing platforms are –
- Google Cloud Platform(GCP): This platform allows users to build deploy and manage services on the same infrastructure as Google.
- Microsoft Azure: It is a cloud computing platform run by Microsoft that offers management and development of applications through global data centers. It provides a wide range of services like software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS).
- Amazon Web Services(AWS): It is a subsidiary of Amazon that provides on-demand cloud computing services on a pay-as-you-go basis. It provides services related to storage, networking, middleware, IoT, etc.
All of this provides a wide range of services for storing, analyzing, and processing data. These platforms provide a lot of benefits to data engineers including enhanced scalability, a wide range of tools for data analysis, and on-demand computing resources. So a good data engineer must have knowledge and experience in the cloud technologies.
5. Data Processing and Analysis
Data analysis is a process of cleaning and processing raw data to extract relevant information in order to take the right business decision. It helps businesses to target better customers, reduce operational costs, get more accurate data, and a lot more. It involves using machine learning and statistical techniques to identify patterns and relationships in data. Some of the tools that you need to learn for data analysis and processing are Apache Pig, Apache Hive, and Presto.
6. Knowledge of Distributed Computing Frameworks
The distributed system is a computing environment in which different components are spread across a cluster on a network. In recent years distributed frameworks have gained immense popularity so they are present everywhere in the data engineering domain. These systems divide across clusters to complete the job more effectively.
Frameworks like Apache Hadoop and Apache Spark can be used for processing massive amounts of data. Further, these provide a foundation for big data applications. So having knowledge of one of these frameworks is really crucial to ace the field of data engineering.
7. Data Warehousing and Storage
Data Warehousing and storage are the two most crucial components of data engineering. With the rapid growth of data, it is really crucial to have an efficient solution that can handle large amounts of data. Data warehousing helps organizations store, analyze and organize chunks of data at a common location. These are analytical tools that are built to support decision-making for users across multiple departments.
Some of the common data warehousing tools are:
- Amazon Redshift: It is a cloud-based fully managed data warehouse by Amazon. It uses the data to provide new business insights to customers.
- Microsoft Azure: It is a cloud computing platform that was launched in 2010 by Microsoft. It provides more than 200 services like data analysis, storage, virtual network, integration, etc.
- Snowflake: It is a data warehousing solution built on top of Amazon Web Services or Azure cloud infrastructure. It allows storage and computation to scale independently. So customers can pay for computation and storage individually.
- Google BigQuery: It is a data warehouse solution that allows the analysis of a huge amount of data. It is a cloud-based service that can be used to process huge amounts of read-only datasets.
To get more information about Data Warehousing and Storage, do read the Top 15 Popular Data Warehouse Tools.
8. Data Pipelines
Data pipelines are the backbone of data engineering. It is a series of connected steps, data processing, integration, and analysis that move data from source to destination. These pipelines are crucial for organizations to extract insights in an efficient manner. Some of the common tools that engineers need to learn in order to build data pipelines are Apache Airflow, Azure Data Factory, Apache Bean, AWS Glue, etc. Also, it is crucial to have knowledge of containerization technologies like Docker and Kubernetes.
9. Stream Processing Framework Knowledge
Stream processing frameworks are used in data applications that require real-time data. So there is a huge demand for candidates who have knowledge of these frameworks. Some of the common Stream processing tools are Flink and Spark streaming. Data engineers must learn how to use Stream processing tools to take their careers to the next level.
10. Practical Knowledge of ETL Frameworks
ETL stands for “extract, transform, load” these are the three processes that jointly move data from one database to multiple databases or in the data warehouse. It helps data engineers to effectively prepare data for the business analysis process. A data pipeline is a collection of data processing elements that migrate data from source to destination and generally from raw format to analytics-ready format.
One of the main tasks of data engineers is to build data pipelines using ETL frameworks. Data engineers must be comfortable with some of the best technologies like Apace Airflow and Apache NiFi.
11. Develop Great Communication Skills
Data engineers are required to communicate with data analysis and data scientists to understand their needs. So to become a data engineer it is crucial to develop great communication skills. Data engineers must know how to develop visualizations and reports to communicate with stakeholders.
12. Building Projects
So till now you must have learned all the important concepts required to become a successful data engineer. Now it is the time to apply this knowledge and skill to test yourself. The last and final step in becoming a successful data engineer. The project must hit all of these domain data warehousing, data analytics, big data tools, and data pipelines. Some of the common projects that you can build to gain some practical insights and enhance your resume are Smart IoT infrastructure, event data analysis, data visualization, and data aggression.
Do Read: Tips to Become a Data Engineer
So these are the twelve steps that you need to follow in order to ace your career in the field of data engineering. It is one of the most in-demand jobs in the field of data and hence, it is a great career choice for data professionals. Learning all these skills will take some time so ensure to learn every day. Build the skills required to become a data engineer and choose this highly-demanding profession.
Become Data Engineer FAQs
1. What qualifications do I need to be a data engineer?
You need to earn a bachelor’s degree in computer science, or any related domain, and have good knowledge in maths, physics, statistics, or a related field. You should have a real-world experience, work on projects, internships, etc.
2. How long does it take to become data engineer?
To become a data engineer, it takes years of practice and experience, to begin your journey as a data engineer, you must start learning Python and earn a bachelor’s degree.
3. Does data engineer need coding?
Data engineers need to have a strong command over Python (mostly preferred programming language) to work on wide range of tasks other than SQL and other skills required.
4. What is the salary of a data engineer?
The salary of a data engineer in India ranges between 3 LPA to 20 LPA with an average salary of 12LPA, it also depends on other factors like skills, experience, etc.
Share your thoughts in the comments
Please Login to comment...