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Career Path: How to Become a Data Engineer

Modern companies rely on data about themselves, their customers and their competitors to stay relevant and ahead of the curve. This is why a Data Engineer is such a valuable profession in the IT world and beyond, as they are the architects who put in place the structure needed to retrieve, store and manage immense amounts of data. It is a highly-technical role as well as highly-collaborative, so those interested in a Data Engineer career path should be well-versed in current and emerging technologies and thrive as a member of a team.

Getting Through the Door


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Career Path: How to Become a Data Engineer

Modern companies rely on data about themselves, their customers and their competitors to stay relevant and ahead of the curve. This is why a Data Engineer is such a valuable profession in the IT world and beyond, as they are the architects who put in place the structure needed to retrieve, store and manage immense amounts of data. It is a highly-technical role as well as highly-collaborative, so those interested in a Data Engineer career path should be well-versed in current and emerging technologies and thrive as a member of a team.

Getting Through the Door

Data Engineering is a career that experts agree is growing and will remain in demand for the foreseeable future. Before entering the job scene, many Data Engineers have a degree in computer science. For those interested in an entry-level Data Engineering position, it is beneficial to spend time in an IT assistant role to gain further mastery with computer programming, building data structures, maintaining databases and software design.

By demonstrating a certain amount of education and experience, a Data Engineer can gain an entry-level position that can lead to a rewarding career. Other key factors potential employers may consider when seeking a Date Engineering include:

  • An understanding of Python, C/C++, Java and Perl for writing code.
  • A working knowledge of Apache Hadoop analytics, such as Hive, Hbase and Mapreduce for data storage and processing.
  • Proficiency in building data structures and managing databases.
  • Demonstrating an interest in big data and what it can achieve.
  • Understanding how proper data infrastructure can affect various tech teams and the business at large.

Degrees and Experience

Those who enter the Data Engineer profession most commonly have a bachelor’s degree in computer science or a related field. Some hold additional certifications from online courses or other training programs, for their own elucidation or as directed by employers.

Like with many technical careers, experience is key with getting a position in Data Engineering. Being able to display an aptitude for the various programs, languages and tools listed above will make a Data Engineering candidate capable of taking on regular tasks and responsibilities of the job, with more experience gained as they earn knowledge through working in an entry-level role.

Working as a Junior Level Data Engineer

Data Engineers who attain a junior-level in their role have demonstrated a growing proficiency in Data Engineering tools, as well as a desire to move forward in their career. Some of the most important tech tools that are key to gaining mastery include SQL, for managing relational databases and Apache Hadoop, to solve problems relative to immense amounts of data. These are common programs used as a Data Engineer on a regular basis, so it is expected that a junior Data Engineer should be comfortable with using these tools while proving a growing understanding of their intricacies.

Moving up the Ranks…

…As a Data Engineer involves a commitment to building sound and secure data structures that help support all levels of data storing, mining, accessing and analyzing used by various teams throughout the company. Data Engineers who want to move forward in their career have to be willing to keep apprised of industry trends, learn and implement new technologies as they emerge, tread uncertain territories and be a consummate team player. Being quick with emerging technologies is key, as a Data Engineer who discovers and puts into service better IT equipment is instrumental to the quantity and quality of data generated and stored that data scientists and others analyze, directly participating in the future of the company.

Advance Your Career: How to Become a Senior Level Data Engineer

Senior level Data Engineers have spent years successfully building and managing intricate and secure data structures. They have a reputation for developing infrastructures that help their colleagues mine and assess quality data that can direct business operations.

Senior Data Engineers also have proven themselves capable of handling growing responsibilities, which can include training junior hires and building structures that have new before been used. With these advances in their career often comes much-earned respect and a higher paycheck.

Study the Core Fields

The world of Data Engineering is comprised of many cross-functioning sectors and by taking an interest in many of these, a Data Engineer will maintain a competitive edge. Some of these fields include cloud and distributed computing, business intelligence, statistical analysis, cognitive computing and marketing. All of these fields rely on data in one way or another and understanding the basic ins-and-outs of each can make you a more effective Data Engineer.

Invest in Yourself

High-tech careers are ever-evolving and competitive. Data Engineers who take time to enhance their skills and deepen their knowledge in the field will see much benefit to their career. This can be done in several ways, including by obtaining professional Data Engineering certifications through coursework offered by companies like Microsoft and IBM. It also never hurts to network and find out industry trends and evolutions. By going the distance to keep your technical skills sharp, employers will take notice and be impressed with your determination and strong work ethic.

Don’t Stop at Data Engineering

Take a look at the other amazing work happening around you to which the work of Data Engineering contributes. One such instance is with data science. After all, from the structures Data Engineers build, data scientists extract and analyze data to understand trends and create solutions, so these two professions are intrinsically linked and are constant collaborators.

Machine learning engineers rely on big data to create AI systems and devices that can complete an action without being prompted to do so. Like with data scientists, the work of Data Engineers directly affects the quality of data a machine learning engineer collects to make their projects a reality.

Data Engineers scope of work affects these and other occupations, so taking the time to better understand these roles and their goals can make a Data Engineer more passionate about their work as well as spark an interest in other careers.

Data Engineer Job Description

Data Engineers develop and maintain infrastructures that allow for the collection and storage of data. This is an important role for any company that relies on data to tell them a story about their operations, their customers and potential customers, which most do. Data Engineers are tasked with turning this data into a format that makes it valuable for analysis and more.

Some of the infrastructure Data Engineers are responsible for building and maintaining include databases and large-scale dataset processors, which allow for the acquisition, mining, modeling and authentication of vast quantities of data. It is other teams that take that data and make it tell a story, but without Data Engineers, the architecture would not be available to collect, store and utilize that important data.

What We Need Your Help With

  • The creation and maintenance of large-scale data structures that help users generate, store and manage big data.
  • The development of tests that allow for the data warehouses to be deemed reliable
  • The design and deployment of more sound data architecture to replace faulty structures.
  • Communicating with team members and other colleagues and managers progress on big data structures using the proper verbiage per audience.
  • Keeping business needs in mind when collaborating on and designing data structures.
  • Using cutting-edge technologies and common scripting languages

We Look For

  • An engineer who consistently demonstrates their proficiency in handling big data.
  • Having a firm grasp on database solution tools, such as SQL, Cassandra and Bigtable
  • Proficiency with programs like Redshift and/or Panoply for data storage and StitchData and/or segment to handle ETL tools.
  • Someone who is curious and always willing to implement the latest and greatest technologies
  • An engineer who is a consummate team player but can also work well alone.
  • Possessing an analytical and logical mindset

These Would Also Be Nice

  • Excellent communication skills
  • Understanding business needs while being client focused
  • Going the distance to make projects a success
  • A willingness to take on new responsibilities
  • Eager to get their hands on new technologies

Senior Data Engineer Career Paths: Where to Go from Here

Data Engineers who have climbed the career ladder and attained a senior level have many options before them. Many are content to stay at the senior level as a Data Engineer until retirement. Others may see this career milestone as an opportunity to move on to another career. Similar careers that Data Engineers have decided to change to have included data science and machine learning engineers, both of which, as stated earlier, are familiar with data structures and the importance of big data.

Career experts and tech leaders have all concluded that the need for Data Engineers will continue to grow, so it is quite possible a senior level Data Engineer will be happy staying where they are and see what developments happen in the field, mentoring junior team members and enjoying the immense knowledge and experience they have gained, while learning new tricks along the way.


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Scala is a strongly, statically typed functional/object-oriented language primarily targeted to the JVM. It brings a lot of powerful programming language concepts out of academia, placing them into the hands of practitioners. A key design emphas...

Java is a statically-typed, cross-platform language. It is concurrent, class-based, and object-oriented. It has minimal implementation dependencies and compiled Java code can run on all platforms that support Java without the need for recompilat...


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