Written by Coursera Staff • Updated on
Big data is changing how we do business and creating a need for data engineers who can collect and manage large quantities of data.
Data engineering is the practice of designing and building systems for collecting, storing, and analysing data at scale. It is a broad field with applications in just about every industry. Organisations can collect massive amounts of data, and they need the right people and technology to ensure it is in a highly usable state by the time it reaches data scientists and analysts.
In addition to making the lives of data scientists easier, working as a data engineer can allow you to make a tangible difference in a world where we’ll be producing 463 exabytes per day by 2025 [1]. That’s one and 18 zeros of bytes worth of data. Fields like machine learning and deep learning can’t succeed without data engineers to process and channel that data.
What does a data engineer do?
Data engineers work in various settings to build systems that collect, manage, and convert raw data into usable information for data scientists and business analysts to interpret. Their ultimate goal is to make data accessible so that organisations can use it to evaluate and optimise their performance.
Listen to some practising data engineers talk about what they do.
These are some common tasks you might perform when working with data:
Acquire datasets that align with business needs
Support the development of data streaming systems
Implement new systems for data analytics and business intelligence operations
Develop business intelligence reports for company advisors
Develop algorithms to transform data into useful, actionable information
Build, test, and maintain database pipeline architectures
Collaborate with management to understand company objectives
Create new data validation methods and data analysis tools
Ensure compliance with data governance and security policies
Working at smaller companies often means taking on many data-related tasks in a generalist role. Some larger companies have data engineers dedicated to building data pipelines. In contrast, others focus on managing data warehouses—populating warehouses with data and creating table schemas to track where data is stored.
What's the difference between a data analyst and a data engineer?
Data scientists and data analysts analyse data sets to glean knowledge and insights. Data engineers build systems for collecting, validating, and preparing that high-quality data. Data engineers gather and prepare the data, and data scientists use the data to promote better business decisions.
Read more: What Is Data Analysis? (With Examples)
Why pursue a career in data engineering?
A career in this field can be both rewarding and challenging. You’ll play an essential role in an organisation’s success, providing easier access to data that data scientists, analysts, and decision-makers need to do their jobs. You’ll rely on your programming and problem-solving skills to create scalable solutions.
The growing importance of data in our society has gained national attention in India, with the government of India pushing the Digital India programme forward and promoting digital development as a key player in India’s economic future. As data continues to be a pillar in many industries, the need for professionals able to understand, manage, and analyse data is expected to grow.
Data engineer salary
Data engineering is a well-paying career because of the high level of technical skill and the need for advanced training. The average salary in India is ₹9,07,000, according to Glassdoor [2]. Individual salaries vary based on location, experience, company size, and job responsibilities.
Data engineer career path
Data engineering is only sometimes an entry-level role. Instead, many data engineers start as software engineers or business intelligence analysts. As you advance, you may move into managerial roles or become a data architect, data scientist, or chief data officer.
How to become a data engineer
With the correct set of skills and knowledge, you can launch or advance a rewarding career in data engineering. Many data engineers have computer science, information technology, or applied math backgrounds. A formal degree, such as from a university or college, can help build a strong quantitative foundation to master data and infrastructure tasks in this quickly-evolving field. It is also popular to earn a postgraduate degree when pursuing a career in data engineering to advance your career and unlock potentially higher-paying positions.
Besides earning a degree, you can take several other steps to set yourself up for success.
1. Develop your data engineering skills.
Learn the fundamentals of data management, integration, modelling, testing, and engineering to increase your chances of success in a career in data science. Several technical skills to consider honing your skills in include:
Coding: Proficiency in coding languages is essential to this role, so consider taking courses to learn and practise your skills. Common programming languages include SQL, NoSQL, Python, Java, R, and Scala.
Relational and non-relational databases: Databases rank amongst the most common solutions for data storage. You should be familiar with both relational and non-relational databases and how they work.
ETL (extract, transform, and load) systems: ETL is the process by which you’ll move data from databases and other sources into a single repository, like a data warehouse. Common ETL tools include Xplenty, Stitch, Alooma, and Talend.
Data storage: Some types of data should be stored differently, especially in big data. As you design data solutions for a company, you’ll want to know when to use a data lake versus a data warehouse.
Automation and scripting. Automation is necessary to work with big data simply because organisations can collect so much information. You should be able to write scripts to automate repetitive tasks.
Data analytics and business intelligence systems: Implementing operational system data flows.
Machine learning: While machine learning is more the concern of data scientists, it can be helpful to grasp the basic concepts better to understand the needs of data scientists on your team.
Big data tools: Data engineers don’t just work with regular data and often manage big data. Tools and technologies are evolving and vary by company, but some popular ones include Hadoop, MongoDB, and Kafka.
Cloud computing: You’ll need to understand cloud storage and cloud computing as companies increasingly trade physical servers for cloud services. Beginners may consider a course in Amazon Web Services (AWS) or Google Cloud.
Data security: While some companies might have dedicated data security teams, many data engineers securely manage and store data to protect it from loss or theft.
Presenting findings to non-technical audiences: Be able to explain what you are designing or fixing and how this will benefit the organisation.
2. Get certified.
A certification can validate your skills to potential employers, and preparing for a certification exam is an excellent way to develop your skills and knowledge. Options include internationally recognised Professional Certificates such as the Associate Big Data Engineer, Cloudera Certified Professional Data Engineer, or Google Professional Data Engineer.
Check out some job listings for roles you may want to apply for. If you notice a particular certification is frequently listed as required or recommended, that might be an excellent place to start.
Read more: 6 Popular Data Analytics Certifications: Your 2023 Guide
3. Build a portfolio of data engineering projects.
A portfolio is often a key component in a job search, showing recruiters, hiring managers, and potential employers what you can do.
You can add data engineering projects you've completed independently or as part of coursework to a portfolio website. Alternatively, post your work to the Projects section of your LinkedIn profile or to a site like GitHub — both free alternatives to a standalone portfolio site.
4. Start with an entry-level position.
Many data engineers start in entry-level roles to build the experience and skills needed for more advanced roles. Roles such as database administrator or database developer can help you build relevant skills and gain industry experience. As you build knowledge and learn from other experts in the field, you will be more equipped to grow within your role and transition into more advanced data careers.
Do I need a degree to become a data engineer?
Having a degree to become a data engineer is unnecessary, though some companies might prefer candidates with at least a bachelor's degree. If you're interested in a career in data engineering and plan to pursue a degree, consider majoring in computer science, software engineering, data science, or information systems.
Some bachelor's degree programmes offer a concentration in data engineering. The B.Tech program in Data Science & Engineering from Manipal Institute of Technology is one of the first in it's kind. Learners explore data analytics, visualisation, machine learning, and more.
Next steps
Whether you’re just getting started or looking to change to a new career, start building job-ready skills for roles in data with the Google Data Analytics Professional Certificate. In under six months, you’ll learn how to perform day-to-day job responsibilities as an entry-level data analyst, critical analytical skills, data management, and organisational techniques, and how to prepare yourself best to enter an exciting new role in this field.
Frequently Asked Questions (FAQ)
While the aspects of a career that make it "good" will always be subjective, data engineering is an in-demand profession that offers a higher-than-average salary and relative job security. While Glassdoor identifies the average salary for data engineers as ₹9,07,000, they also can earn additional compensation of up to an extra ₹4,00,000 per annum 2.
Yes, data engineers must code. Common coding languages that data engineers should know or be familiar with include Python, Java, R, SQL, NoSQL, and Scala.
Data engineers can work from home, though some employers might prefer or require employees to work on-site. Nonetheless, the nature of their work means that many data engineers can theoretically do it from home.
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