Data Engineering Careers

Best Places to Find Data Engineer Jobs on the Market

Find your next job in data. Check out the best job offers for data engineer from top companies.

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About Data Engineer Jobs

Jobs for data engineers are on the rise as big data becomes increasingly important in business. A data engineer is responsible for designing, testing and maintaining data pipelines and architecture. They convert raw data into operational information that can be easily interpreted by users, such as data analysts and data scientists, in their respective organisations.

Firstly, brush up on your technical skills. Data engineering generally requires a strong background in computer science and experience with programming languages such as Java, Python and SQL.

Secondly, familiarise yourself with the most popular data management platforms, such as Hadoop, Spark and Cassandra.

Finally, consider pursuing a data engineering certification from a recognised institution. With the right skills and knowledge, you'll be well on your way to landing your dream job as a data engineer.

A data engineer is generally an information technology worker whose key job is designing, testing and maintaining data pipelines and architecture. They convert raw data into operational information that can be easily interpreted by users, such as business analysts and data scientists, in their respective organisations.

Additionally, a data engineer is needed since most companies and organizations are data-intensive. They integrate, merge and clean data and the structures used for analytics purposes. The amount of data an engineer uses may vary in every organization, depending on its size.

The main role of a data engineer is to process data that data scientists cannot use for operational purposes to a more suitable form. There are other responsibilities which include:

  • Generating and maintaining databases.
  • Development of key validation methods and tools for data analysis.
  • Avail data sets consistent with the organisation's goals.

Depending on the workplace, the roles of a data engineer vary. For instance, in a small organisation, there is the development of data infrastructure while improving the already existing system. With the help of a data engineer, enterprises can optimize their performance greatly since every bit of data is accessible.

Big data engineer jobs require essential data skills for you to thrive in the field:

Programming Languages or Coding

Programming is an incredibly valuable skill required in data engineering jobs. There are programming languages that many employers would like their data engineers to be familiar with, like python, C and C++. 

Strong SQL Skills

SQL knowledge is an important technical skill; it helps you extract data from multiple sources and convert them into a usable form, which is essential in making informed business decisions. It is also essential for you to be familiar with the diverse versions of the SQL syntax.

Data Warehousing Solutions

Data warehousing involves collecting data from several sources and availing it in a centralized database in an interpretable hierarchy. The database is known as a warehouse. Examples of data warehouses common to data engineers include; Redshift, Azure and BigQuery.

Cloud Data Computing

Creating a cloud to store data and ensure it is available for use is one of the significant tasks for big data engineer jobs. Therefore, this becomes a crucial skill to learn when working with huge data amounts.

Many skills are relevant to the engineering field; therefore, before starting as a data engineer, ensure you've familiarized yourself with a fair percentage of them. 

To pursue a career in information engineering, you must get the right certifications. A certification is a validation of your skills to your potential employers, and preparing for the certification exam is a great way to boost your skill set range.

Some popular data engineer jobs certifications that will give your career an edge include; IBM certification in information engineering and Google cloud certified data engineer. If you study several job lists you would like to apply for; you will find the required certifications, which will be a great place to start.

Surprisingly, there is no clear path to becoming a data engineer. However, there are known effective methods of developing skills for use in data engineer jobs, including university education, self-study, and project-based learning.

Most data engineers acquire a huge percentage of their skills while working in junior data engineer jobs and work their way up.

Data engineering is a well-paying carrier. The tasks in the organisation's data section are professional labor-intensive. That has thus increased the demand for the data engineer's skills and experience. Additionally, working as a remote data engineer affects the amount of salary you earn.

However, the data engineer's payment depends on the experience level. The average entry-level salary is £110,970, the mid-level data engineer's average salary is £138,950 and the senior data engineer level has an average wage of £154970.

When looking to be employed in the data engineer jobs field, interview preparations could be overwhelming on most occasions. Being well-prepared for interviews is critical to stand a chance of landing a job. The most effective way of preparing is researching the most asked questions in such interviews.

The following are some questions that are mostly asked in data engineer interviews;

  • What is data engineering? - This question might seem easy and basic, but it isn't; the interviewer would like to check your level of specificity.
  • Why did you choose data engineering as a career? - This inquiry aims to know your interests and motivations behind this carrier path.
  • How is an operational database different from a data warehouse? - How you answer this question shows your commitment to the job.
  • Data quality - quality poses an incredibly critical threat to the decision-making of a company. The value of data is downgraded by issues like; duplicated or missing data and inconsistent and inaccurate data. This issue is addressed in most data engineering jobs by introducing an ETL job before data is used in generating relevant reports or models.
  • Data management - due to the large volumes of data that keep growing, consolidating them becomes a big challenge. Data becomes increasingly unmanageable as time goes by, requiring a lot of time to process and store the new incoming data.
  • Human mistakes - the human factor is one of the most eminent challenges in the engineering of data. Unfortunately, this challenge has little to no solution since all data engineer jobs are done entirely by humans.
  • Lack of a clear strategy - inability to create and stick to a clear strategy could lead to failure of the organisation's critical process. However, if there is any need for change, it should be done by integration to avoid meddling with the company's objectives.
  • Lack of end-user understanding - most data engineers often lose sight of the real objective of the project without a clear understanding of the user group. Therefore, before any progress is made, ensure that your team understands the nature of the users of the respective system.