Big Data Engineer Career

DICE has identified big data engineering as the fastest-growing occupation of 2020, predicting a growth of 50% between 2015 and 2020. This is an excellent time to become a data engineer because the demand for them is high, companies of all sizes are competing for them, and salaries are rising. What follows is a complete guide to a profession in big data engineering and will tell you everything you need to know to get started.

The Big Data Engineering: What Is It?

You're probably familiar with the term "big data," which refers to the massive quantities of user data and transaction records created by websites and services as diverse as The New York Times and cloud storage providers like Amazon and Facebook. The sheer volume and variety of big data makes it impossible for humans to effectively process and draw conclusions from it. Big data engineering is where it gets interesting.

The field of big data engineering is dedicated to the development of systems for collecting and making sense of massive amounts of information, such as the millions or even billions of clicks, taps, likes, swipes, shares, and purchases that businesses and consumers make every day. They accomplish this by constructing data pipelines and focusing on the ETL (Extract, Transform, and Load) procedure, as well as by designing and managing data infrastructures like big data frameworks and databases.

Can You Explain What a Big Data Engineer Does?

Data processing infrastructure is what big data engineers focus on, so it's helpful to think of them as data architects. They're in charge of creating, maintaining, and improving the system that gets the job done. Big data engineers must be well-versed in SQL and NoSQL databases, as well as database solutions like Cassandra, Bigtable, and Hadoop, to accomplish this.

Big data engineers possess these abilities, allowing them to construct and maintain data workflows that other data professionals, such as data scientists and data analysts, can use to test hypotheses and analyze data. Data engineers facilitate the transformation of big data into actionable information.

Understanding the Big Data Engineer's Function

When it comes to big data, the software developers are big data engineers, who are also known as data engineers. The exact duties of a data engineer may vary from one company to the next, but the core competencies and experience levels needed are typically the same.

The Role of the Big Data Engineer

Data engineers are the ones who are in charge of converting massive amounts of raw data into more manageable formats for further processing and analysis. Expertise in numerous technical areas, such as SQL and AWS, is required for this task. The job description will typically provide clues as to what a candidate needs to know in order to qualify, including the types of programming languages a data engineer needs to know, the company's preferred data storage solutions, and background information on the teams the data engineer will work with.

According to job postings for data engineers, ideal candidates should have experience with:

• Construct and keep up a top-notch data pipeline
• Build and update a database management system
• Collect and organize massive data sets to serve organizational needs.
• Find, create, and put into action ways to better our own internal operations
• Improve scalability by streamlining data delivery and redesigning the underlying infrastructure.
• Construct the framework for efficient data extraction, transformation, and loading from multiple sources using SQL and Amazon Web Services.
• Create analytic tools that make use of the data pipeline to reveal useful information about customer acquisition, operational efficiency, and other critical business performance indicators.
• Help resolve data-related technical issues and meet data infrastructure needs by collaborating with internal and external stakeholders.
• Build analytic and data scientist team-members' data tools

ng the information. In computer science, data pipelines describe how information is moved and stored. Raw data from a SaaS platform like a CRM system or email marketing tool is stored in a data warehouse and then analyzed using analytics and business intelligence tools.

Estimating the Expense of Hiring a Big Data Engineer

The demand for big data engineers has resulted in a significant increase in salary. Salary information for software engineers can be found in Hired's 2019 Salary Guide, where data engineers are reported to earn above-average wages. The average salary for a data engineer in New York City is $132,000. In San Francisco, that number rises to $151,000.

ZipRecruiter reports that the average entry-level salary for a data engineer is $97,000.

Future Opportunities for Data Engineers

Due to the novelty of the field, there is no predetermined order of steps to take in order to become a successful data engineer. Data engineers, like many other professionals in the technical sector, typically begin their careers with a bachelor's degree in computer science, applied mathematics, statistics, or a related field and then expand their education with coursework in programming languages, information technology, or data analytics.

Aspiring data engineers will use these abilities and credentials to apply for entry-level data engineering positions or to secure positions in other departments that will allow them to move laterally. Data engineers often start their careers in IT because that is where they can learn the most about a company's data needs and the processes involved in gathering, organizing, and utilizing that data.

How Does One Navigate the Professional Waters of Big Data Engineering?

There is a wide range of data engineer job descriptions out there. The volume of data collected, the company's size, and the sophistication of its data operations all play significant roles in determining the scope of these positions.

• In smaller organizations, a data engineer may be in charge of everything from setting up data sources to administering analytics software. In other words, they would be responsible for all aspects of a data science project, including database design, data pipeline construction, and data warehouse management.
• Companies of a medium size have both data scientists and data engineers, and the two disciplines work together to create the bespoke software tools required to achieve specific big data analytics objectives. They manage the infrastructure of data integration tools that move information between disparate data sources and a central data repository. These pipelines can be used for straightforward information transfer or for more complex purposes.
• Typical duties for a data engineer at a large company include creating table schemas, populating analytics databases, and tuning them for speedy analysis. Data must be gathered from various sources, transformed into an easily analyzed format, and then loaded into a data warehouse, all of which are steps in the ETL process.

Steps to Taking the Big Data Engineer Exam

Hiring managers prefer to work with big data engineers who have relevant experience and education, can demonstrate their proficiency with the necessary tools, and can adapt to and thrive in the face of change. Typical qualifications listed in data engineering job descriptions are provided below.

Education

Although a degree in computer science, mathematics, statistics, physics, or a related field is not required to become a data engineer, the majority of those in the field do have at least a bachelor's. If you don't have a degree in data engineering or have one in a related field, but still want to work in the field, you may want to enroll in a course or bootcamp that teaches not only the technical skills you'll need, but also the analytical and critical thinking abilities that will help you adapt to new situations.

Skills

Data engineering is a highly technical field that necessitates in-depth familiarity with a variety of programming languages, automation and scripting tools, database management systems, data processing, and cloud computing.

Data engineers need expertise in many areas, including data warehousing, ETL, data APIs, machine learning, distributed systems fundamentals, and interpersonal communication and teamwork.

Qualifications

Although a bachelor's degree is sufficient for entry-level work in data engineering, having additional qualifications, such as vendor-specific certifications or a broader Certified Data Management certificate, can help set applicants apart and reassure hiring managers of their experience with industry tools and best practices. Additionally, certifications are available from IBM, Cloudera, Microsoft, and Oracle.

Essential Languages and Technologies

Data engineers use many platforms and tools related to data science and data analytics, such as Apache Spark, Apache Hadoop, Cloudera, Scala, MongoDB, MapReduce, Amazon Web Services, Azure, and Perl, in addition to programming languages like Python, SQL, R, C++, and Java.

Advice for Aspiring Big Data Engineers

1. Take a Course
2. Get Certified
3. Build a Portfolio
4. Start From the Bottom
5. Use Your Time Wisely and Focus on the Most Useful Project You Can
6. Network Like Crazy

Getting started in data engineering can be done in a variety of ways. The following is a typical path taken by those who have successfully obtained positions in big data engineering.

1. Take a Course

Data engineers are software engineers who specialize in data analysis and statistics. To get started as a data engineer, you should brush up on your programming skills and familiarize yourself with languages like Python, SQL, and R. The fundamentals of data science, including analysis, statistics, and working knowledge of data pipelines, frameworks, and architectures, as well as the most popular data management and storage tools, can all be acquired through participation in an online course or bootcamp.

2. Get Certified

Consider getting certified in data management to give yourself an advantage in the job market. Not only will this demonstrate to potential employers that you are dedicated to staying abreast of industry developments, but it will also help you learn more about topics like Warehouse and business intelligence systems; data ethics and governance; data security; management of metadata; Data management certifications are available from the Global Data Management Community, and vendor-specific certifications are available from companies like Microsoft, Oracle, and IBM.

3. Build a Portfolio

Even if you don't have a degree or years of experience in the field, a solid portfolio of work can help you stand out to potential employers. A portfolio can show prospective employers that you have the experience and skills necessary to be a big data engineer, whether you've worked on projects in your spare time or performed some of the duties of the position in another role.

4. Start From the Bottom

Finding an entry-level position in data engineering can be challenging; as such, you may want to consider interning in the field or beginning your career in a related field that will expose you to the challenges and develop the skills necessary to succeed as a big data engineer. Starting out as an IT analyst, for instance, will teach you SQL and data warehousing, allow you to construct data pipelines, and pave the way for a lateral move.

5. Make Progress on Any Appropriate Task You Can

It's easy to believe that you can only acquire data engineering experience by working in a data engineering capacity. However, this is not the case, as data engineers share many of the same skills as software engineers, data analysts and scientists, quality assurance engineers, and other IT professionals within an organization; data engineering bootcamps provide hands-on experience with real-world projects; and there is nothing preventing an individual from collecting and transforming publicly available datasets.

6. Network Like Crazy

Big data engineers often hear about new job openings through recommendations from colleagues. In light of this, it is useful to cultivate a group of professional allies comprised of both mentors and peers in one's field. Take advantage of the network of mentors and career counselors available to you if you are currently enrolled in a bootcamp or an online course. Make an effort to network with the company's data engineers and upper management. And remember to always be on the lookout for conferences and other networking events in your field.

The Big Data Engineer's Frequently Asked Questions

Do you need more answers to your questions about becoming a big data engineer? Take a look at the info we have on hand to address your concerns.

What is the level of difficulty to enter the field of Big Data Engineering?

It is not simple to become a big data engineer because the field is highly technical and demands proficiency in a number of languages as well as an in-depth knowledge of database architecture and the ability to keep up with emerging technologies and data warehousing solutions.

Those who are critical thinkers, analytical problem solvers, and who want to make a real difference in their organizations will find the training and the job rewarding despite the challenges they present, especially given the field's rapid evolution and its growing importance across all industries.

Is Higher Education Necessary for a Career as a Data Engineer?

It is preferred, but not required, to have a bachelor's degree in mathematics, statistics, computer science, or a related business field. All that is required is completion of an online bootcamp or course that teaches the fundamentals of advanced statistics and programming languages useful for data mining, querying, and, in some cases, utilizing big data SQL engines.

Data engineers, on the other hand, are expert software engineers who are familiar with database architecture and the development of data pipelines. Finding a university course that caters to this is still challenging, so a self-paced online bootcamp in data science or data engineering is a better option. Python, R, and SQL are the main languages used by data engineers, and you'll learn these and other skills like data pipeline development and machine learning here.

Must a Big Data Engineer Have Coding Experience?

Unfortunately, there's no getting around the fact that Python, SQL, and Java proficiency is essential for success in the field of big data engineering. If the idea of learning to code seems overwhelming, there is an increasing number of data engineering bootcamps that provide short, self-paced courses to introduce complete beginners to the programming languages necessary to get the most out of a more advanced data engineering bootcamp.

The time required to become a Big Data engineer is discussed.

How long it takes to become a big data engineer is highly variable, depending on factors such as where a candidate goes to school and whether or not they work in a related field before making a career change. With a weekly study time commitment of 15-20 hours, a 6-month online bootcamp can teach students everything they need to know to be successful in the role of big data engineer.

Can You Work Your Way Up to Big Data Engineer Without Prior Experience?

Though it is possible to break into the field of data engineering with no experience, candidates improve their chances of being hired by gaining internship experience or gaining data engineering experience in another position.

The most important thing is to gain practical experience in data engineering in any way possible, even if it isn't listed on your resume, so that you can demonstrate to potential employers that you have what it takes to do the job through projects and case studies.