Written by Alex Gurevich – Last updated:
This guide is all about how to become a data engineer. It includes information on what kind of degree or education is best-suited for getting started as a data engineer and potential career paths and outcomes.
Businesses worldwide are inundated with large amounts of data that must be processed and analyzed to help decision-makers provide solutions in operations, such as marketing, sales, production, distribution, and staffing. These businesses rely on data engineers to design and maintain systems to manage and optimize this data flow.
This translates into a high demand for data engineers. In 2019, data engineering was the fastest-growing job, according to the 2020 Dice Tech Job Report. Growth stood at 50 percent at the time of that report. Since 2016, the report further states that data engineers’ demand has far outgrown supply. In 2019, data engineer Carlin Eng wrote about job searching in this field and found that many companies had “aggressive hiring goals” for data engineers. When it came to the biggest challenge for these companies, “hiring was number one on the list.”
Data Engineer Degree
To begin a career as a data engineer, you would typically need to earn at least a bachelor’s degree. Four-year degree programs you might consider include the bachelor of science in data science, bachelor of science in data analytics, or a bachelor of science in computer science. More than 100 US colleges and universities offer degree programs in data science.
Common courses found in a bachelor’s degree program in data science or data analytics are:
- Big data
- Data mining
- Data modeling
- Applied statistics
- Data warehousing
- Business analytics
- Data visualization
- Database systems
- Database management
In these courses, you could learn about real-time analytics, mining software, machine learning applications, business intelligence, database design, data security practices, programming languages, data patterns, data structure, file management, data manipulation, and network modeling. Some bachelor’s degree programs contain internship opportunities so you can apply concepts studied in the curriculum in real-world situations.
Some companies might prefer a master’s degree, even for non-managerial positions. A master’s degree would also typically be required for advancement in the field. Master’s degrees you could pursue include master of science in data science, master of science in data analytics, and master of science in analytics. You could also consider a master of science in information systems with a concentration in database management or a master of business administration (MBA) with a concentration in data analytics.
At the master’s level, courses tend to focus on more advanced topics in predictive analysis, data trends, decision support, statistical analysis, machine learning theory, data architecture, and forecasting. Graduate internships in data science or data analytics are also available. Large companies such as GEICO and Gap, Inc. offer internships wherein you will experience hands-on learning opportunities in data retrieval, forecasting, statistical modeling, and systems development. Other companies such as Amazon, IBM, Capital One, and PayPal have hired MS in data analytics students and graduates for internships and full-time jobs.
Case study examinations and data analytics projects are generally major parts of master’s degree studies in this field, providing greater opportunities for hands-on learning and real-world exposure. Other practices that support a master’s degree program curriculum in data analytics, data science, or a similar area include conferences, symposia, online and live presentations, and career fairs. Hence, you have the chance to network and interact with professionals, faculty, and peers.
How to Become a Data Engineer
With the need to know about complex programming languages and coding, data transformation processes, technical design, and data processing and manipulation, few “entry-level” data engineer jobs are offered. Mainly, at least a bachelor’s degree is required to enter this field, as formal degree programs essentially cover many of the basics needed to begin in the field.
Bootcamps offer an accelerated way to learn various aspects of data engineering. These provide hands-on, project-focused learning methods for data mining, architecture, programming, warehousing, etc. These bootcamps effectively boost your knowledge, expand your skills, and brush up on advanced concepts to help demonstrate your abilities to prospective employers. After completing a bootcamp, you might obtain a position and then pursue your degree while working. Or, showing a hiring manager that you attended a bootcamp could show not only initiative but your interest in and dedication to the field.
Certifications are designed to display your abilities and depth of knowledge in programming, analytics, data systems design, and many other areas. These reinforce your skillset within industry-specific applications and systems. Technological companies and professional associations generally offer certifications. Google alone, for instance, offers eight certifications in and relating to data engineering, including Cloud Network Engineer, Machine Learning Engineer, Data Engineer, Cloud DevOps Engineer, and Collaboration Engineer.
Examples of other data engineering certifications follow:
- Amazon: AWS Certified Data Analytics – Specialty
- Data Science Council of America: Associate Big Data Engineer (ABDE)
- Data Science Council of America: Senior Big Data Engineer (SBDE)
- SAS: Certified Big Data Professional
- Cloudera: Cloudera Data Platform (CDP) Generalist
- Microsoft: Azure Data Engineer Associate
- Databricks: Certified Professional Data Engineer
What Does a Data Engineer Do?
The primary responsibility of a data engineer is to develop and use systems to help companies transform raw data into accessible information that can be analyzed and processed. This allows those in management positions to make decisions and create solutions. They apply their knowledge of programming and coding to develop databases, servers, processing systems, and data warehouses.
The duties of a data engineer would typically include optimizing data delivery systems, analyzing internal data processes, designing data analytics tools, maintaining data pipeline systems, and creating complex data sets.
According to the IT magazine CIO, the responsibilities of a data engineer would look like this:
- Develop, construct, test, and maintain architectures
- Align architecture with business requirements
- Data acquisition
- Develop data set processes
- Use programming language and tools
- Identify ways to improve data reliability, efficiency, and quality
- Conduct research for industry and business questions
- Use large data sets to address business issues
- Deploy sophisticated analytics programs, machine learning, and statistical methods
- Prepare data for predictive and prescriptive modeling
- Find hidden patterns using data
- Use data to discover tasks that can be automated
- Deliver updates to stakeholders based on analytics
Data Engineer Career Paths
An actual data engineer job description might look like this, based on an actual posting:
Senior Data Engineer
The data engineer will effectively extract, transform, load, and visualize critical data. They will build and ensure the accuracy of data pipelines driving faster analytics through data. This individual will work in an agile environment partnering with business, software application teams, and data scientists to understand their data requirements and ensure all the teams have reliable data that drives effective business analytics. The successful candidate will be a self-starter comfortable with ambiguity, with strong attention to detail, and enjoy working with a large scale of data.
- Build data solutions from the design phase to completion and ensure they meet specific requirements
- Build data pipelines, engineer complex new data sets, assess data quality, perform data engineering or ETL for data marts, visualizations, or data science models
- Query large data sets for ad hoc exploration, analysis, or testing
- Build a deep understanding of Spark (Databricks) and Python (Pyspark) to support your technical design solutions
- Build a deep understanding of the Azure Cloud Platform and stay updated on new capabilities, positioning yourself as a subject matter expert
- Build a deep understanding of Azure Data Factory, Databricks, ADLS, and Synapse (SQL Data Warehouse), so you can identify and recommend improvements to designs and strategies across the Azure technology stack.
- Support Agile Scrum teams with planning and scoping technical, analytic solutions, including time estimates for development and testing
- Participate in Agile scrum teams delivering data ingestion, validation, engineering, modeling, visualization, and analytics solutions.
- Engage with Technical Architects and technical staff to determine the most appropriate technical strategy and designs to meet business needs
- Liaise with data architecture, data engineers, and other technical contracting resources to work through technical dependencies, issues, and risks.
- Engage with business stakeholders to understand required capabilities, integrating business knowledge with technical solutions
- Communicate complex technical information to business customers and project teams in an effective and concise manner
- Adheres to applications security procedures, change control guidelines and coding structures, Sarbanes-Oxley IT, and business requirements
- Performs other duties as assigned
- Complies with all policies and standards
Other Example Career Paths
Skills typically highlighted for data engineers include organizational, analytical, communication, time management, problem-solving, and critical thinking.
After obtaining a degree in data science or a related area, you could choose from a few other career paths in addition to data engineering. You might focus on data infrastructures as a data architect or oversee creation and maintenance as a database administrator. A few other career options to consider are:
- Data scientist
- Data manager
- Big data engineer
- Machine learning engineer
- Business intelligence developer
According to Payscale.com, the average annual salary for a data engineer is just over $93,000. Senior data engineers realize a yearly salary of nearly $125,000. New York, Seattle, and San Francisco are among the top cities for data engineer salaries, Payscale further reports.
It can take four or more years to become a big data engineer. The duration depends on the educational and career path you choose. If you choose to go to college for a bachelor's degree program, then it will take about four years.How long does it take to become data engineer? ›
It can take four or more years to become a big data engineer. The duration depends on the educational and career path you choose. If you choose to go to college for a bachelor's degree program, then it will take about four years.Is it hard to become a data engineer? ›
In all honesty, becoming a data engineer can be hard. But once you've nailed the key skills and landed your first job, you'll find plenty of freedom to develop your dream role. You'll get to choose what you're working on and when, and will rarely be told what tools to use.Can I learn data engineering in 3 months? ›
Once you have all the required skills and experience, it takes an average of 3-6 months of job training to become a data engineer. Getting a certification as a Associate - Data Science Version 2.0 will help you to earn more as a data engineer.What should I study for data engineer? ›
- Coding. Coding is a highly valued skill that is a requirement for a majority of data engineering positions. ...
- Data warehousing. ...
- Knowledge of operating systems. ...
- Database systems. ...
- Data analysis. ...
- Critical thinking skills. ...
- Basic understanding of machine learning. ...
- Communication skills.
Data science careers require mathematical study because machine learning algorithms, and performing analyses and discovering insights from data require math. While math will not be the only requirement for your educational and career path in data science, but it's often one of the most important.Is data engineer job stressful? ›
Is data engineering stressful? Many factors force data engineers to work long, irregular schedules that take a toll on their well-being. In fact, 78% of survey respondents wish their job came with a therapist to help manage work-related stress.Does data engineer require coding? ›
As in other data science roles, coding is a mandatory skill for data engineers. Besides SQL, data engineers use other programming languages for a wide range of tasks.Do data engineers need math? ›
Often, even if you don't have a computer science or math background, you can still get into data engineering by getting an analyst or project manager position first. From there you can start pushing for more and more work in the data engineering space.How do I become a data engineer with no experience? ›
- Develop your data engineering skills. Learn the fundamentals of cloud computing, coding skills, and database design as a starting point for a career in data science. ...
- Get certified. ...
- Build a portfolio of data engineering projects. ...
- Start with an entry-level position.
Several employers also prefer candidates with at least a bachelor's degree in computer science or a relevant field like data science. However, you don't necessarily need a degree to become a data engineer; a data engineering bootcamp, a certification, or a self-study path can equally be sufficient.Is it too late to learn data science at 30? ›
Whatever your age, it's never too late to pursue your dreams of becoming a qualified data scientist.Is 30 too old for data science? ›
It is not too late to pursue a career in machine learning or data science, even in your late 20s or early 30s. Here are some reasons why: These fields are still growing rapidly. Machine learning and data science are still emerging areas with many new opportunities.How to get job in Google as data engineer? ›
- Experience in Python and SQL languages.
- Knowledge of cloud platforms.
- Understanding of Machine Learning (ML) concepts.
- Basics of Java and Scala programming.
- Understanding of SQL and NoSQL databases.
- Knowledge of data warehousing and data modeling.
The job is in high demand, according to a recent Dice study. The report predicted data engineering to be one of the fastest-growing jobs in technology, with a predicted 50% year-over-year growth in the number of open positions.How much does a Google Certified Professional data engineer make? ›
|Annual Salary||Hourly Wage|
What Education Does a Data Engineer Need? Given the knowledge demands of this role, data engineers need to have a strong educational background. Usually, you start on this career path with a bachelor's degree in computer science, software engineering, information technology, or a related field.Which is harder data scientist or data engineer? ›
Data science is easier to learn than data engineering.
Well there's simply more resources available for data science, and there are a number of tools and libraries that have been built to make data science easier.
If you want to become a data engineer, salary is an important factor to consider. Data engineers are typically well-paid compared to many other jobs. However, actual salaries can vary significantly depending on the specific industry, the size of the organization, and the level of experience of the data engineer.What is the salary of data engineer with 1 year experience? ›
Data Engineer salary in India with less than 1 year of experience to 7 years ranges from ₹ 3.3 Lakhs to ₹ 20.9 Lakhs with an average annual salary of ₹ 7.7 Lakhs based on 19.9k latest salaries.
The disadvantage of being a data engineer is that they may work very hard but fail to get the desired results within a considerable amount of time. Big data tools are still emerging, which might make it difficult to mine, analyze and monitor data properly.How many hours a week does a data engineer work? ›
Data engineers typically work a full-time schedule at 40 hours a week, Monday to Friday. They may be required to work extra hours or on weekends, too.Can anybody become data engineer? ›
Anyone who enters this field will need a bachelor's degree in computer science, software or computer engineering, applied math, physics, statistics, or a related field. You'll also need real-world experience, like internships, to even qualify for most entry-level positions.Is Python required for data engineer? ›
Data engineers are expected to be fluent in Python to be able to write maintainable, reusable, and complex functions. This language is efficient, versatile, perfect for text analytics, and gives a legit foundation for big data support.Do data engineers need to know C++? ›
Data scientists often use C++ to write big data frameworks and libraries. These are then used by other languages as well.Who gets paid more data engineer or data scientist? ›
Data Engineer vs Data Scientist - Salary.
|Position||Average Salary in India||Average Salary in USA|
|Senior Data Engineer||15 LPA||135K USD|
|Data Scientist||10.5 LPA||120K USD|
Data Engineers have become a rare commodity.
“Even the hottest Silicon Valley companies are unable to achieve a one-to-two ratio. […] You don't have enough engineering talent out there. It's very expensive.” says Tomer Shiran, the CEO and co-founder of Gremio, a developer of big data middleware.
Data Analyst analyzes numeric data and uses it to help companies make better decisions. Data Engineer involves in preparing data. They develop, constructs, tests & maintain complete architecture. A data scientist analyzes and interpret complex data.Can a non IT person become data engineer? ›
Although it is true that some IT professionals seek to advance their skills in analytics, this field is not only open to people with a background in programming and IT. Many successful Data Scientists began their Data Science careers without prior coding knowledge or IT experience.Why is data engineering so tough? ›
Data engineering is hard because it focuses on storing, transforming, and moving statistics, requiring learners to master various technologies and tools. You'd better expect data engineering to be challenging as it is an intensely technical field.
There is a shortage of data engineers but several companies need this professional leading to a mismatch between the number of data engineers needed and the available candidates. Even with the job market unbalanced, it is weird to think why a professional without an IT background can't easily get a data engineer job.How to become a data engineer in 6 months? ›
- Get familiar with the basics. If you're not already familiar with the basics of data engineering, the first step is to get up to speed. ...
- Choose a focus area. ...
- Build a portfolio. ...
- Connect with the community. ...
- Get a job. ...
- Keep learning.
Pilli Siddharth Srivatsav, a 15-year-old boy is leaving everyone in awe with his achievements at a very young age.How much coding is required for data engineering? ›
As a data engineer, you must have strong coding skills as you'd need to work with multiple programming languages. Apart from Python, other popular programming skills include . NET, R, Shell Scripting, and Perl. Java and Scala are vital as they let you work with MapReduce, a vital Hadoop component.Is data science dead in 10 years? ›
So, until and unless we find a way to not use data itself, data science as a field is not going to be obsolete anytime soon. However, many believe that since a data scientist's daily tasks are quantitative or statistical in nature, they can be automated, and there will not be a need for a data scientist in the future.Is data science too stressful? ›
The field of data science is fast-paced, demanding, and challenging. Learning to perform your responsibilities correctly can take some time too and that can add to your stress. However, you need to remember that you are not a machine and that working is important but it is not worth sacrificing your health.How stressful is data analyst? ›
Several data professionals have defined data analytics as a stressful career. So, if you are someone planning on taking up data analytics and science as a career, it is high time that you rethink and make an informed decision.Can a 40 year old become a data scientist? ›
You can become a data scientist at any age if you're willing to put in the work.Can I do data science if I'm bad at math? ›
The answer is yes! While data science requires a strong knowledge of math, the important data science math skills can be learned — even if you don't think you're math-minded or have struggled with math in the past.What is the average age of a data engineer? ›
Data Engineer Age Breakdown
Interestingly enough, the average age of data engineers is 40+ years old, which represents 45% of the population.
Average Google Data Engineer salary in India is ₹ 40.9 Lakhs for experience between 2 years to 6 years. Data Engineer salary at Google India ranges between ₹ 12.0 Lakhs to ₹ 93.5 Lakhs.What is the salary of Google Data Engineer 2? ›
Data Engineer salaries at Google can range from ₹7,00,000 - ₹20,00,000 per year.What is data salary in Google? ›
|Years of Experience||Total||Base|
A data engineer is an IT worker whose primary job is to prepare data for analytical or operational uses. These software engineers are typically responsible for building data pipelines to bring together information from different source systems.Who pays the most for data engineers? ›
Data Engineer Salary at Amazon
A data engineer is among the highest-earning positions at Amazon when compared to other job roles such as data scientist, software engineer, or machine learning engineer.
I believe that it's not only unlikely but impossible, thanks to the way AI is trained. There are skills (for e.g. these data science skills) AI will never be able to replace, no matter how advanced.How tough is Google Data Engineer certification? ›
Final Words. The GCP Data Engineer Certification will take two hours and include 50 questions. So, if you've done all of the above-mentioned focused studying and gone through the practice examinations, the actual exam shouldn't be too difficult.Is Google Data Engineer certification hard? ›
The professional data engineer exam is difficult and requires much practice and studying. It is designed by Google to target those with hands-on experience and adequate knowledge of their services.What is Google's highest paying certificate? ›
The Google Data Analytics Professional Certificate is one of the most valuable Google career certifications you can get. Certified data analysts get an entry-level salary of $67,900 per year and can grow to more than $110K per year once they get 10+ years of working experience.Can I learn data engineering in 4 months? ›
Estimated 4 months to complete
You'll master the AWS data engineering skills necessary to level up your tech career. Learn data engineering concepts like designing data models, building data warehouses and data lakes, automating data pipelines, and managing massive datasets.
- Get familiar with the basics. If you're not already familiar with the basics of data engineering, the first step is to get up to speed. ...
- Choose a focus area. ...
- Build a portfolio. ...
- Connect with the community. ...
- Get a job. ...
- Keep learning.
If you want to become a data engineer, salary is an important factor to consider. Data engineers are typically well-paid compared to many other jobs. However, actual salaries can vary significantly depending on the specific industry, the size of the organization, and the level of experience of the data engineer.Is data engineer a well paid job? ›
A beginner-level Big Data Engineer's salary is approximately ₹466,265 per year. As a Big Data Engineer in the early stages of their career or as a Junior Big Data Engineer (with 1 to 4 years of experience), a typical salary is ₹722,721 per year.Is 35 too late to become a data scientist? ›
Whatever your age, it's never too late to pursue your dreams of becoming a qualified data scientist. Learn how to succeed in this profession below.What is the salary of 3 years big data engineer? ›
Highest salary that a Big Data Engineer can earn is ₹20.0 Lakhs per year (₹1.7L per month). How does Big Data Engineer Salary in India change with experience? An Entry Level Big Data Engineer with less than three years of experience earns an average salary of ₹6.7 Lakhs per year.Why do data engineers make so much? ›
That's because Big Data Engineers, like other development and tech professionals, are in demand all over the country and across almost every industry. But many factors can influence how much you'll earn.What is the salary of big data engineer in Amazon? ›
The typical Amazon Data Engineer salary is ₹14,00,000 per year. Data Engineer salaries at Amazon can range from ₹1,23,721 - ₹35,00,000 per year. This estimate is based upon 125 Amazon Data Engineer salary report(s) provided by employees or estimated based upon statistical methods.Are data engineers overworked? ›
While advanced in technical Data Literacy, data engineers require support to improve their organizational Data Literacy. They get “overwhelmed with developing and maintaining enterprise data systems” and end up overworked.What is the lowest salary of data engineer? ›
Data Engineer salary in India ranges between ₹ 3.3 Lakhs to ₹ 20.9 Lakhs with an average annual salary of ₹ 7.6 Lakhs.What is the basic salary for data engineer in US? ›
Data Engineer Salaries
The national average salary for a Data Engineer is $96,909 in United States.