Who Is a Data Engineer?

Specialists in the field of data engineering are among the most desirable employees on the global labor market and are sought after by companies from varied industries. They have an exciting and future-oriented career ahead of them, guaranteeing not only professional development but also a very good salary.

Who is a data engineer? What are his responsibilities? What is data engineering, and why is it so popular? What skills and experience do you need to apply for the job?

Here’s a thorough introduction to the term.

What does a data engineer do?

A data engineer designs, builds, and maintains the infrastructure and systems used to store, process, and analyze large data sets. They work closely with data scientists and analysts to ensure that the data they are working with is accurate, reliable, and accessible on time.

Some of the key responsibilities of a data engineer include the following:

1. Designing and building data pipelines that extract data from various sources and move it into storage systems.
2. Building and maintaining data storage and processing systems, such as data warehouses, data lakes, and NoSQL databases.
3. Ensuring that data is appropriately formatted, cleaned, and integrated before it is loaded into storage systems.
4. Designing and implementing data security and data access control mechanisms.
5. Writing and maintaining scripts and software to automate data management tasks.
6. Optimizing data storage and processing systems for performance and scalability.
7. Collaborating with data scientists, analysts, and software engineers to develop and implement data-driven solutions.

Skills needed to become a data engineer

To become a data engineer, you will typically need to have a combination of technical and analytical skills, including:

1. Strong programming skills

Data engineers need to be proficient in at least one programming language, such as Python, Java, Scala, and SQL. They use these languages to write scripts and programs that extract, clean, and process data.

2. Familiarity with big data technologies

Data engineers work with large sets of data, so they need to be familiar with technologies that are designed to handle them, such as Apache Hadoop, Apache Spark, and Apache Kafka.

3. Knowledge of databases

Data engineers need to be familiar with different databases, including relational databases such as MySQL or PostgreSQL and non-relational databases such as MongoDB or Cassandra.

4. Experience with data warehousing and data lakes

Data engineers design and build systems that store and process data. They need experience with data warehousing and data lake technologies, such as Amazon Redshift, Snowflake, and Apache Hive.

5. Strong analytical skills

Data engineers need to be able to understand and analyze complex data sets, so they need to have strong analytical skills.

6. Familiarity with cloud computing

Many companies store and process data in the cloud, so data engineers need to be familiar with cloud computing platforms, such as AWS, Azure, and GCP.

7. Problem-solving abilities

Data engineers often have to troubleshoot problems with data pipelines and storage systems, so they need to be able to think critically and solve problems efficiently.

8. Strong communication and collaboration skills

Data engineers often work closely with data scientists, analysts, and software engineers, so they need to communicate effectively and collaborate on projects.

9. Continual Learning

Data engineering is rapidly evolving, so data engineers should be comfortable learning new technologies and continuously updating their skills.

Data engineer vs. data scientist - how do they differ?

Some people equate a data engineer's profession with a data scientist's career. In practice, these are two different positions, although people, who occupy them, work closely together. A data engineer is responsible for developing tools that allow data processing, being an architect and developer who ensures its availability.

A data scientist, in turn, applies various statistical techniques and methods related to machine learning and uses them to process and analyze data. He is also often responsible, for example, for developing models that indicate what knowledge can be extracted from a specific data source.

Why start a career in data engineering?

A career in data engineering can be enriching for several reasons:

1. High demand

Data is becoming increasingly important to businesses and organizations, and the demand for data engineers is growing rapidly. According to the Bureau of Labor Statistics, employment in computer and information technology occupations is projected to grow 11 percent from 2019 to 2029, much faster than the average for all occupations.

2. High earning potential

Data engineers often command high salaries, with the average salary for a data engineer in the US being around $120,000 annually.

3. Opportunities for advancement

As a data engineer, you will be working on the cutting edge of technology and data science, and you will have the opportunity to take on more complex projects and responsibilities over time. With the growing importance of data, many organizations are looking for experienced data engineers to help lead their data teams.

4. Exciting and varied work

Data engineering is a multifaceted field that offers many opportunities to work on many different projects and with diverse technologies. You will work with large data sets, building data pipelines, designing data architectures, and solving complex data problems.

5. Impactful role

Data engineers play a critical role in organizations as they build the infrastructure to collect, process, and store data, which can be used for decision-making, product development, research, and much more.

6. Flexibility

Data engineering can be a remote-friendly career. You can work from anywhere and don't have to be tied to a specific location for your job.

Data engineer career path

Many companies operating in the IT industry can hire a data engineer, including outsourcing entities providing services in this field. In practice, however, such a person is needed wherever companies shape their decisions based on various data and information. Therefore, employment is possible in enterprises from many sectors, including financial, analytical, e-commerce, medical or administration-related.

The data engineer position requires continuous improvement of one's competencies and staying up to date with all the news from the world of data, i.e., collecting, processing, and managing them.

There is a hierarchy of positions typical for the IT industry. You can work successively as a junior, an independent, and a senior data engineer. Promotion is possible by obtaining the right experience. A data engineer can develop toward becoming a prominent data engineer.

The most typical issues in a data engineer job

Working with data, a person usually sees small businesses and large corporations encountering similar problems. Which are the most common ones?

1. The data could be of better quality, e.g., the postcode field is in the wrong format, and the place where a word should be is a number.
2. The human factor will always matter, and the most frequent problem involves the time availability of a given person.
3. Lack of documentation, very often domain knowledge.
4. Problems with system access or permissions.
5. Cutting costs may leave you with a demanding job, poor tools, and no people to help you.
6. Deadlines - a company wants the job done as soon as possible, and your focus is on time instead of quality.
7. If a lot of people work on one project, it may lead to communication chaos, which will make your work difficult.

Summing up

Apart from the above, a sound data engineer should also have specific soft skills and personality traits. Having highly developed communication skills, teamwork orientation, the ability to interact effectively with others, management skills, and good work organization is desirable. In addition, a data engineer should be goal-oriented, flexible, quickly adapt to new projects, and willing to constantly improve one's qualifications.

Contact us

Choose Sunscrapers for your data engineering or data science project to benefit from our versatile experience and world-class expertise in Python.

We can help you unlock the hidden potential of your data to improve decision-making processes, automate tasks, and boost operational efficiency.

Contact us at hello@sunscrapers.com

Read more

1. How To Become A Data Scientist?
2. Data science outsourcing: key risks and benefits