Data Engineer Jobs

Data scientists and data engineers frequently collaborate as members of an analytics team. Engineers provide data for analysis by data scientists, who then apply analytics such as predictive modelling, machine learning, and data mining.

Moreover, data engineers provide aggregated data to corporate executives, analysts, and other end users for further analysis and application to the betterment of business operations. The work of a data engineer encompasses both structured and unstructured data.

When information is "structured," it can be stored in a database or other structured repository. Text, photos, audio, and video files all fall under the category of "unstructured data," which does not map neatly into typical data schemas.

Data engineers need expertise in both data architecture and applications, but they approach the two very differently. The data engineer's toolkit also includes numerous big data technologies, such as open source data input and processing frameworks.

Data engineers are responsible for gathering and preparing information for usage by analysts and scientists. There are primarily three roles that they play.

General-focus data engineers

Generalist data engineers operate in small groups to handle all aspects of the data lifecycle, from collection to analysis. They might be better at their jobs than the majority of data engineers, but they're probably not as well versed in system architecture.

Anyone with a background in data science who aspires to work in data engineering might do well in a more generalist capacity.

For example, a small company in a major city might hire a generalist data engineer to build them a dashboard that shows how many products were sold on each day last month and how many are expected to be sold next month.

Pipeline-focus data engineers

These data engineers generally participate in complex data science projects spanning multiple dispersed systems and work on a mid-sized data analytics team. This position is more common in medium- to large-sized businesses.

For example, a regional company may launch a pipeline-focused project to provide a platform for data scientists and analysts to explore metadata related to its services.

They may employ a prediction algorithm to analyse past performance information, such as the total level of overheads spent in performing daily output tasks.

Database-focus data engineers

Analytics database creation, upkeep, and data populating are the purview of these data engineers. Usually found in larger organizations with multiple data repositories, this position is vital to ensuring data integrity and consistency.

Engineers use extract, transform, and load (ETL) techniques to build pipelines, fine-tune databases for effective analysis, and design table schemas. The goal of ETL is to consolidate information from multiple data sources into a single repository.

Creating an analytics database is a database-centric project for a large, multi-state or nationwide company.

The data engineer would not only build the database, but also write the necessary programming to move information from the primary application database into the analytics database.

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As data engineering encompasses several domains, it stands to reason that numerous data engineering roles exist. One data engineer could be more concerned with the programming and development aspects, while another could be more interested in the analysis.

Following are some examples of possible data engineering positions:

Analytical engineers

To analyze data and integrate disparate data processing systems, an analytical engineer uses SQL and NoSQL databases and programming languages like Java, Python, and R. Database administrators focus on keeping things running smoothly, while analytical engineers look for methods to improve workflow.

Database administrator

Having acquired data, the job of a database administrator is to put it into a system that can be accessed and updated easily.

They do more than just set up the database systems; they test them and tweak them for peak performance and maximum safety. Database engineers are responsible for the efficient collection and storage of information.

Data architect

Throughout an organization, data professionals rely on the infrastructures that builders create for their daily work. They will collect information from several online, mobile, and social media resources, and store it in a central location.

Here, we'll discuss how to ace a technical interview, the skills that employers are looking for, and the factors that will determine your future employment prospects.

Show that you are ahead of the latest developments

You must be able to learn on your own initiative and be eager to study and experiment with new tools on a continual basis if you want to get hired for a data engineering position, as technological advancements are happening at a breakneck pace.

That doesn't mean you have to blindly follow every fad that comes along; rather, it's meant to stress the importance of keeping an open mind.

Scrub up on the fundamentals

When looking for a data engineering position, you should have a firm grasp of database administration, data warehousing, data lakes, Big Data, and REST APIs.

The results of a job interview would be less than ideal if you were unable to explain the 3Vs of Big Data or the qualities of a data warehouse.

Prepare adequately for your interview

You should prepare for the application process by answering certain (very basic) technical questions.

Most managers in data engineering will test your knowledge of SQL window functions, generators, broadcasting, and Python list comprehensions by asking you to develop a star schema for a specific scenario.

Showcase your interpersonal skills

If you want to succeed in any field, it's important to develop skills that will look well on your resume. Many companies would choose with the second alternative.

Soft skills, such as project management, public speaking, recordkeeping, and the ability to moderate and organize events, are highly sought after by employers.

Continue to develop even while you have a job

To become a data engineer, data literacy is the single most crucial skill you can possess.

Most organizations have relatively inadequate data documentation, so it's important to take the time to learn exactly what data your company has, where it came from, how it was acquired, the ETL process (if any), the nuances in the data, and anything else that's significant.

This is the single most significant consideration for helping you design effective data projects. It's possible that you're already engaged in data engineering.

You already have most of the abilities need to acquire the other ones. Employers do place some weight on extracurricular activities, but prior work experience is much more influential in their decision-making.

Below is a list of all the things you need to know to become a data engineer.

Embrace the role of data-driven analytics

It's important to get in the appropriate frame of mind before attempting to understand data engineering. And by "the correct mindset," this is fundamentally a hunger for intellectual growth. Curating useful insights from data is a very new discipline, and it has achieved a fascinating apex only

recently. Therefore, it is probable that you may face challenges that will need additional effort; nevertheless, if you possess the requisite strong willpower, you will easily excel in this field.

Get a degree in the field

Entering this field requires a bachelor's degree in computer science, software or computer engineering, applied mathematics, physics, statistics, or a closely related field. Most entry-level jobs also require some kind of practical experience, such as an internship, before you can even apply.

If you don't plan on majoring in computer science or information technology in college, you should still take some classes in data structures, algorithms, database management, or coding. Get as much education as you can.

Collaborate with classmates on personal projects, attend hackathons with pals, or form study groups to bolster your resume for future job applications.

Develop your skills

It is essential that you become proficient with SQL, one of the most widely used computer languages in the field of data engineering.

As most information today is kept in relational databases, this is a need. SQL is used by engineers for data querying, while SQL engines like Apache Hive are used for analysis.

Data engineers should be fluent in Python or R, two programming languages that are useful in the context of statistical analysis and modelling. Familiarity with Spark, Hadoop, and Kafka is also helpful.

In addition to fluency in the target language, desirable qualifications also include familiarity with database architectures, familiarity with machine learning, knowledge of data warehousing solutions, data pipeline and data mining techniques, and familiarity with cloud platforms such as Amazon Web Services.

Data engineers need to have their finger on the pulse of developments in data management technology.

Get employed in an entry-level job

Even if your first job has nothing to do with engineering, it could nevertheless teach you vital lessons about how to tackle data organization problems. If you want to succeed in your first career, you'll need to be able to think outside the box and come up with novel solutions to difficulties.

Data engineers, as you will rapidly discover, are not lone rangers. Management, data scientists, and data architects are all taken into account instead because this is a team effort.

You may also learn how to collect, analyse, and apply data to improve business operations as a result of your participation in this program.

Add to your list of certifications

In order to move up the ranks in the field of data engineering, it is generally necessary to earn relevant credentials. There are many vendor-specific certifications available, including those for Oracle, Microsoft, IBM, and Cloudera, among others.

Because there are numerous certificates out there, it's important to do your research on the careers you're interested in and consult with mentors to figure out which ones are worth pursuing.

Consider getting a Masters Degree in a related field

While many engineers achieve success without formal education beyond high school, those interested in becoming data engineers or data scientists may find that a master's degree in computer engineering or computer science is an excellent starting point.

A master's degree in data engineering is not necessary for all careers. Some companies may be willing to forgo a degree in favour of candidates who can demonstrate the necessary skills and knowledge through job experience alone.

Stay abreast of new data engineering tools

Snowflake for warehousing, dbt for ELT, Airflow for orchestration, etc. are just a few examples of the numerous new technologies that have entered the market recently. Be on the lookout for these items and build some projects around them for experience.

Don’t underestimate the role of cloud computing

Every business will ultimately be forced to move their data-related processes to the cloud. And the likely leaders of this effort will be data engineers.

Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure are the three most prominent rivals in the industry of offering cloud computing service platforms.

If you want to work in data engineering, you should familiarize yourself with cloud computing and build several projects that demonstrate your familiarity with at least one of the three major platforms.

Is data engineering a good career?

If you have a keen eye for detail, thrive while working within strict engineering rules, and enjoy the challenge of turning raw data into useful insights, a career as a data engineer may be right up your alley. Working as a data engineer can lead to high salaries and stable employment opportunities.

Are data engineers in demand?

Yes, according to recent data, the hiring growth rate of professionals in this job has increased by nearly 35% since 2015.

How much do data engineers earn?

The average salary for a Data Engineer is R 654 238 per year in South Africa.