Big data engineering is one of the most in-demand roles in tech, with DICE naming it the fastest growing occupation of 2020 at 50% year-over-year growth. As companies of all sizes compete for data engineers and salaries become increasingly competitive, there’s never been a better time to enter the field. Read on to find out everything you need to know about a career in big data engineering.
What Is Big Data Engineering?
You’ve probably heard of the concept of big data—the troves of user information and recorded actions generated by social media platforms such as Facebook, Twitter, and TikTok, ecommerce stores like Amazon, and a whole range of websites and services ranging from The New York Times to cloud storage hosts. Big data is so overwhelming in breadth and quantity that it is impossible for humans to parse through in its raw form to glean insights. This is where big data engineering enters the picture.
Big data engineering focuses on the infrastructure that allows people to collect and organize all that data—the millions to billions of clicks, taps, likes, swipes, shares, and purchases—in a way that is usable. They do this through building data pipelines, designing and managing data infrastructures such as big data frameworks and databases, handling data storage, and focusing on the ETL (Extract, Transform, Load) process.
What Does a Big Data Engineer Do?
If big data engineering is about the infrastructure used to process data, it helps to think of big data engineers as data architects responsible for building, maintaining, and improving that infrastructure. To do this, big data engineers need an in-depth knowledge of SQL and NoSQL databases, as well as database solutions such as Cassandra, Bigtable, and Hadoop.
With these skills, big data engineers build and maintain data workflows, which enable other data professionals such as data scientists and data analysts to hypothesize, test, and analyze the collected data. In other words, data engineers make it possible for big data to become usable.
About the Role of Big Data Engineer
Big data engineers, also commonly referred to as data engineers, are the software programmers of the field of big data. While the job description of a data engineer might slightly differ from organization to organization, the skills and responsibilities required tend to be similar across the board.
Big Data Engineer Job Description
Data engineers are responsible for transforming large amounts of data into formats that can be processed and analyzed. This requires significant technical skill, including knowledge of multiple programming languages and SQL and AWS technologies. While the skill level required varies from junior data engineering roles to more senior roles, the job description will usually contain clues as to what a candidate needs to know in order to qualify, such as the types of programming languages a data engineer needs to know, the company’s preferred data storage solutions, and context on the teams the data engineer will work with.
Based on common data engineer job descriptions, candidates can expect to:
- Create and maintain optimal data pipeline architecture
- Create and maintain a data management system or systems
- Assemble large, complex data sets that meet business requirements
- Identify, design, and implement internal process improvements
- Optimize data delivery and re-design infrastructure for greater scalability
- Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and AWS technologies
- Build analytics tools that utilize the data pipeline to provide actionable insights into customer acquisition, operational efficiency, and other key business performance metrics
- Work with internal and external stakeholders to assist with data-related technical issues and support data infrastructure needs
- Create data tools for analytics and data scientist team members
Big Data Engineer Responsibilities
Data engineers are responsible for building and maintaining an organization’s data infrastructure, including databases, data warehouses, and data pipelines. A common data engineer responsibility includes the transformation of data into a format that is useful for analysis.
This starts with cleaning, organizing, and processing raw, unstructured data. Data pipelines refer to the design of systems for processing and storing data. These systems capture, cleanse, transform and route data to destination systems, taking raw data from a SaaS platform such as a CRM system or email marketing tool and storing it in a data warehouse so it can be analyzed using analytics and business intelligence tools.
Big Data Engineer Salary
As demand has increased for big data engineers, so have their salaries. A 2019 report from Hired found that data engineers are among the most well-compensated software engineers, with a New York-based data engineer commanding an average salary of $132,000, while a similar role in San Francisco earning $151,000.
For entry-level data engineering roles, the national average salary is $97,000, according to ZipRecruiter.
Data Engineer Career Path
Big data engineering is a relatively new profession, which means there isn’t one set path to a data engineering career. Similar to other technical professions, data engineers often get their start with a bachelor’s degree in computer science, applied mathematics, statistics, or a related field and supplement their studies with courses in programming languages, IT, or data analytics.
With these skills and certifications in hand, aspiring data engineers will either apply to junior positions in data engineering or land another entry-level role in a division that will enable a lateral move. It is not uncommon for data engineers to get their start in IT departments because this grants them exposure to an organization’s data needs and how data is collected, organized, and utilized.
What’s It Like Working as a Big Data Engineer?
Data engineer job profiles vary widely between companies. The scope of these roles depends largely on the size of the company, the maturity of its data operations, and the volume of data collected.
- Small companies: A data engineer on a small team may be responsible for every step of data flow, from configuring data sources to managing analytical tools. In other words, they would architect, build and manage databases, data pipelines, and data warehouses—basically doing the work of a full-stack data scientist.
- Mid-size companies: In a mid-sized company, data engineers work side by side with data scientists to build whatever custom tools they need to accomplish certain big data analytics goals. They oversee data integration tools that connect data sources to a data warehouse. These pipelines either simply transfer information from one place to another or carry out more specific tasks.
- Large companies: In a large enterprise with highly complex data needs, a typical data engineer job spec requires data engineers to focus on setting up and populating analytics databases, tuning them for fast analysis, and creating table schemas. This involves ETL (Extract, Transfer, Load) work, which refers to how data is taken (extracted) from a source, converted (transformed) into a format that can be analyzed and stored (loaded) into a data warehouse.
What It Takes To Become a Big Data Engineer
Hiring managers typically look for big data engineers who have received some formal training in the field, can show they have the skills to do the job and are capable of adapting and evolving to new technologies and facing new challenges. Below are some of the requirements commonly listed in data engineering job postings.
Many data engineers hold at least a bachelor’s degree in computer science, math, statistics, physics, or a related field, although this isn’t a prerequisite for becoming a data engineer. Those whose degrees aren’t related to data engineering or who don’t hold a degree at all should consider an online course or bootcamp that equips candidates with not only the skills needed to perform the job of a data engineer but also the analytical and critical thinking skills needed to be adaptive.
Data engineering is, at its core, a technical profession that requires deep knowledge of programming languages, automation and scripting, databases and data processing, understanding database architectures, and cloud computing.
Other essential skills data engineers need include: deep knowledge of data warehousing solutions, a strong understanding of ETL tools and data APIs, knowledge of machine learning algorithms, an understanding of the basics of distributed systems, and the ability to clearly communicate and collaborate with various teams within an organization.
While not strictly necessary to land a job in data engineering, additional qualifications—such as vendor-specific certifications or a broader Certified Data Management certificate—can help set job applicants apart and assure hiring managers of their experience of industry tools and best practices. IBM, Cloudera, Microsoft, and Oracle also offer vendor-specific certifications.
Languages and Technologies To Know
In addition to programming languages such as Python, SQL, R, C++, and Java, data engineers work with many data science and data analytics-related platforms and tools such as Apache Spark, Apache Hadoop, Cloudera, Scala, MongoDB, MapReduce, Amazon Web Services, Azure, and Perl.
How To Become a Big Data Engineer
There are many different paths to a career in data engineering. Below is a common route taken by those who have landed big data engineering jobs.
1. Take a Course
Data engineers are first and foremost software engineers who also possess skills in data analysis and statistics. Start by firming up your programming skills and learning programming languages used by data engineers such as Python, SQL, and R. An online course or bootcamp will help you build these foundational skills, develop your understanding of data analysis and statistics, and equip you with deep knowledge of data pipelines, frameworks, architectures, and commonly used data management and storage tools.
2. Get Certified
To get a leg-up on the competition, consider earning data management certifications—these are useful in two key ways: they show hiring managers that you are proactive in staying on top of the latest tools and technologies, and they will help you develop a deeper knowledge of areas such as data ethics and governance, data security, metadata management, and warehousing and business intelligence. The Global Data Management Community offers data management certifications, while vendors such as Microsoft, Oracle, and IBM offer certificates specific to their platforms and tools.
3. Build a Portfolio
In a competitive job market, a strong portfolio of projects will showcase your skills, even if you lack formal training or industry experience. Whether you’ve worked on projects in your own time or performed some of the duties of a big data engineer in a different role, a portfolio offers evidence to hiring managers that you have what it takes to do the job.
4. Start From the Bottom
Junior data engineering positions can be hard to come by, so consider applying to data engineering internships or starting in an entry level role in a related field that will give you exposure and experience with the problems and skills a big data engineer deals with. For example, starting in an IT analyst role will equip you with knowledge of SQL and data warehousing, give you opportunities to build data pipelines, and facilitate a lateral career change.
5. Work on Any Relevant Project You Can
It’s easy to get stuck in the thinking that you can only gain data engineering work experience in a data engineering role. But this simply isn’t true—data engineers have overlapping skills with software engineers, data analysts and data scientists, quality assurance engineers, and many other IT professionals within an organization; data engineering bootcamps offer opportunities to work on real-world projects, and there’s nothing stopping an individual from collecting and transforming publicly available datasets.
6. Network Like Crazy
One of the most common ways in which big data engineers learn about job opportunities is through word-of-mouth and referrals. Because of this, it’s important to build out a network of industry mentors and peers who, in addition to sharing job news, can also offer professional guidance. If you’re currently enrolled in a bootcamp or online course, make the most of the support network of mentors and career counselors available to you. If you’re working for an organization, proactively reach out to data engineers and managers within the company. And keep an eye out for industry conferences and meet-ups where you can grow your network.
Big Data Engineer FAQs
Still have questions about what it takes to become a big data engineer? Check out our answers to frequently asked questions.
How Hard Is It To Become a Big Data Engineer?
As a highly technical profession that requires knowledge of multiple programming languages, a deep understanding of database architecture, and the ability to stay on top of new technologies and data warehousing solutions, the training required to become a big data engineer is not easy.
However, given the rapid evolution of the field and its growing importance across all industries, the difficulty of both the training and the job can prove rewarding for critical and analytical thinkers, problem-solvers, and those who want to have a meaningful impact on organizations.
Do You Need a Degree To Be a Data Engineer?
A bachelor’s degree in mathematics, statistics, computer science, or a business-related field is helpful—but not required. All you need is an online bootcamp or course that provides a foundation of advanced statistics and programming languages that can be used to mine and query data, and in some cases, use big data SQL engines.
More importantly, data engineers are skilled software engineers who understand database architecture and how to build data pipelines. Since it is still relatively hard to find a university curriculum that supports this, a better option is self-paced learning via an online bootcamp that specializes in data science or data engineering. This will teach you the main programming languages used by data engineers (Python, R, SQL) as well as machine learning, building data pipelines, and finding data warehousing solutions.
Is Coding Required for a Big Data Engineer?
There’s no getting around it—big data engineering is a highly technical field that requires in-depth knowledge of programming languages such as Python, SQL, and Java. If the prospect of learning to code is intimidating, a growing number of data engineering bootcamps offer accessible, self-paced short courses that introduce complete beginners to the programming languages needed to get the most out of a more advanced data engineering bootcamp.
How Long Does It Take To Become a Big Data Engineer?
The length of time it takes to become a big data engineer largely depends on the educational path a candidate takes and whether they first spend time in an adjacent profession before making a lateral transition. An online bootcamp that equips students with all the skills needed to do the job of a big data engineer normally takes around 6 months to complete, with a 15-20 hour/week study commitment.
Can You Become a Big Data Engineer With No Experience?
While it’s possible to land a role as a data engineer with no prior experience, candidates usually increase their odds of getting a foot in the door by completing internships or building data engineering experience while in other roles.
Even if your CV doesn’t list a prior data engineering job, the key is to get hands-on experience in whatever capacity you can so that you have projects and case studies to show future hiring managers that you have what it takes to do the job.
Ready to switch careers to data engineering?
Data engineering is currently one of tech’s fastest-growing sectors. Data engineers enjoy high job satisfaction, varied creative challenges, and a chance to work with ever-evolving technologies. Springboard now offers a comprehensive data engineering bootcamp.
You’ll work with a one-on-one mentor to learn key aspects of data engineering, including designing, building, and maintaining scalable data pipelines, working with the ETL framework, and learning key data engineering tools like MapReduce, Apache Hadoop, and Spark. You’ll also complete two capstone projects focused on real-world data engineering problems that you can showcase in job interviews.
Check out Springboard’s Data Engineering Career Track to see if you qualify.