What is a Data Engineer?

Data engineering has become an increasingly important role in the tech industry. With their invaluable ability to help organizations manage, clean and structure their data, it's no surprise that many companies are looking to invest heavily in these professionals.

But what is data engineering, exactly? What does a data engineer do?

In this blog post, we will explore the key responsibilities required by a data engineer, how their work contributes to business success, and how you can become a successful data engineer yourself.

What data engineers do

A data engineer develops, builds, maintains, and manages data pipelines. This requires working with large datasets, databases , and the software used to analyze them – including cloud systems like AWS or Azure.

The primary focus of a data engineer is to ensure that data flows smoothly from its source to its destination efficiently and securely. The data engineer is the first line of data cleaning and wrangling. They are responsible for:

• Ensuring the accuracy of data.
• Promoting data quality.
• Transforming data into more useful formats.

This can involve writing code to automate processes, leveraging machine learning algorithms to detect patterns or anomalies in data, or utilizing algorithms to identify correlations.

Data engineering: practice overview

Data engineering is the practice of transforming raw data into useful information. It requires a deep understanding of data architectures, data warehousing, databases, and analytics tools. The goal is to create an efficient system for collecting, processing, analyzing, and visualizing large amounts of data from various sources. This can involve:

• Creating custom software applications.
• Designing data warehouses.
• Developing ETL pipelines.

Data engineering requires a combination of technical skills such as programming languages (e.g., Python, Java), distributed systems (e.g., Hadoop, Spark), and databases (e.g., PostgreSQL, MongoDB). It also requires a strong understanding of business needs, as engineers seek to build reliable data pipelines that deliver useful insights for the team or company at large.

Data engineer roles and responsibilities

Work on data architecture

A data engineer's primary responsibility is to design, construct, maintain and troubleshoot an organization's data architecture. This includes selecting the appropriate technology for a company's needs and writing code for required customizations.

Additionally, they will develop effective storage solutions that can handle large amounts of data in a timely and efficient manner. As the data engineer is responsible for ensuring that all data is stored securely, they must ensure investments in security measures are made and regularly maintained.

Collect and store data

Data engineers collect and collate data from multiple sources, ensuring its accuracy and integrity when stored. This involves connecting to:

• External databases
• APIs
• Other storage solutions like Amazon S3

Additionally, they may need to extract unstructured data from text files, emails, and social media posts. Data Engineers must be able to identify and cleanse any corrupt or outdated data to ensure accuracy.

Conduct research

Data engineers often work with data scientists to conduct exploratory research and implement new technologies. This could include:

• Creating algorithms for machine learning.
• Conducting A/B testing.
• Using Natural Language Processing (NLP) to understand customer sentiment.

At the end of the research phase, they will then create a model that can be used to analyze data faster and more accurately. They may be required to work with data scientists to get the right data points required to build an accurate model. This means that they will be involved in some feature selection and feature engineering.

Create models and identify patterns

Data engineers also create models to identify customer behavior patterns or market trends. This often involves using big data tools such as Hadoop or Spark to process large datasets quickly. Additionally, they may need to develop predictive analytics models to forecast future customer behaviors or predict potential risks.

Automate tasks

Data engineers are also responsible for automating processes and tasks which were previously done manually. This could include creating scripts to move files from one location to another or developing algorithms to process data faster. Automation is essential for organizations, as it helps them save time and money while eliminating human error.

For example, data engineers can use data pipeline automation software like Apache Airflow to automate Python scripts that clean raw data before storing it in data warehouses.

Improve on current skills

Data engineers must stay up to date on the latest technologies and trends within their field. This requires continual training, certification programs, seminars, and reading technical blogs or journals. Additionally, they must be able to communicate effectively with other teams to ensure their designs meet the company's goals.

(Check out the best data-related certifications to earn.)

Data engineer skills and qualifications

Data engineers require a unique set of skills and qualifications to be successful in their roles. In addition to having an educational background in software engineering, computer science, or IT, data engineers must also possess a strong technical knowledge base.

Data engineer technical skills

A data engineer would be expected to have the following skills:

• Understanding of programming languages such as Python, Scala, and JavaScript
• Knowledge of big data tools such as MongoDB, Kafka, and Hadoop
• Understanding of data warehouse and Extract, Transform, Load (ETL) tools like Amazon Redshift
• Familiarity with cloud computing tools such as AWS, Azure, and GCP
• Experience in automation and scripting
• Knowledge of machine learning
• Expertise in data transformation tools such as Hevo Data and Talend
• Knowledge of basic data visualization in Excel and Tableau

Data engineer non-technical skills

Soft skills are also important for data engineers. They should have good problem-solving skills, an eye for detail, the ability to work independently and collaboratively as part of a team, strong communication and interpersonal abilities, and an aptitude for learning new technologies quickly. Data engineers must also have the motivation to stay up to date on industry trends and keep their skills sharp, as the field is constantly changing and evolving. With a strong grasp of the latest trends, data engineers should be able to use their technical knowledge to think outside the box and develop unique solutions to address data-related issues.

By honing these data engineering skills and qualifications, you can become a valuable asset to companies looking for innovative ways to leverage their data.

How does a data engineer differ from a data scientist?

A data engineer designs, builds and maintains a company's data infrastructure, including databases or data warehouses. They ensure that accurate and timely data is accessible to the team or application that needs it. This requires a deep understanding of designing, implementing and maintaining complex data systems.

On the other hand, data scientists use the data infrastructure created by a data engineer to analyze trends and patterns in that data. This data analysis could include anything from:

• Creating advanced algorithms.
• Uncovering insights about customer behavior.
• Predicting future outcomes.

In short, data scientists are focused on answering questions, while data engineers create the systems to answer those questions. Data engineering and data science are related but distinct fields.

Data engineers are also responsible for ensuring that data platforms are secure and compliant with industry standards, while data scientists focus on analyzing the data once it is usable.

Why pursue a career in data engineering?

There are many reasons to pursue a career in data engineering, here are just a few:

High demand

With the data engineer job market projected to grow 21% from 2018 to 2028, data engineering is becoming one of the most in-demand roles in the tech industry.

More companies are starting to understand the importance of leveraging dating to achieve goals. As such, companies are increasingly interested in hiring qualified data engineers to help them manage, organize, and secure their data before data scientists can make sense of them.

High salary

Because data engineering is such a sought-after role, it also tends to pay well. According to Glassdoor, the average salary for data engineers in the United States is $116,722 annually.

This high salary stems from the fact that data engineering requires a broad range of sought-after skills and specialized knowledge. Combined with the high demand for such a unique set of data skills, the compensation salaries in the US remain high.

(Explore other IT/tech salaries.)

High impact

Data engineering can have a profound impact on organizations. That's because data engineers manage large amounts of data and organize them for better downstream analysis. Clean data that comes from good data engineering will lead to greater data integrity, which results in more accurate and reliable insights from data scientists.

This means that you get to be part of the decision-making process, which can result in organizations making better decisions for their stakeholders. At the end of the day, this can lead to:

• Higher levels of customer satisfaction
• Increased revenue
• Improved operational efficiency
• And much more

Continuous learning

Data engineering is an ever-evolving field that requires professionals to constantly stay on top of the latest trends and technologies. As such, data engineers must understand programming languages, databases, analytics tools, and data visualization techniques to stay ahead of the curve.

Variety of industries

Data engineering isn't limited to any one industry or sector. Instead, it can be found across various industries, from finance and healthcare to retail and manufacturing. This allows data engineers to explore various opportunities and find the right fit for their skillset and interests.

Related questions

What challenges do data engineers face?

Challenges faced by data engineers include the need to keep up with ever-changing technologies, tools, platforms, and data sets; maintain a secure environment for the storage and analysis of data; and create systems that are highly available, scalable, reliable, and flexible.

Additionally, they must be able to utilize predictive analytics techniques to provide meaningful insights from large datasets.

Who does a data engineer work with?

A data engineer works with various stakeholders, including business analysts, software developers, database administrators, and other IT professionals. They often collaborate on projects to ensure the data is properly understood, stored, and analyzed appropriately.

Additionally, they interact with end users who require access to the data for various purposes.

Do data engineers code?

Data engineers must understand coding languages like SQL, Python, and Java. They use these languages to build data pipelines and write scripts for extracting, transforming, and loading (ETL) datasets into various databases.

Is data engineering a good career?

Data engineering is a highly rewarding career path with excellent job growth potential. Data engineers are in high demand, and those with the right skills can expect an impressive salary. Additionally, data engineers have the opportunity to work on projects that can have a real impact on businesses and help them make better decisions.

If you enjoy problem-solving and working with technology, data engineering can be a great career choice.

What are some common tools and technologies used by data engineers?

Data engineers often use several tools and technologies to build pipelines, extract and manipulate datasets, analyze data, and create reports. Some of the most common tools data engineers use include:

• Apache Airflow
• Amazon Redshift
• Hadoop
• Python
• Java
• SQL
• Tableau
• Talend
• Google Cloud Platform
• Git
• Amazon Web Services

Additionally, they may use software development tools such as Jenkins, Git and Docker to automate processes and manage deployments.

How do data engineers manage scalability issues with data processing?

Data engineers use several techniques to address scalability issues when dealing with large datasets. They may utilize distributed computing frameworks such as Hadoop and Apache Spark, which can process data faster by distributing it across multiple nodes.

Additionally, they may leverage container technologies such as Kubernetes and Docker to scale applications efficiently.

Finally, they may also employ automation tools such as Jenkins, Apache Airflow and Git to automate data pipelines, which can help reduce manual labor and time spent on repetitive tasks.

Is data engineering a stressful job?

Data engineering can be stressful due to the need to solve complex problems, maintain secure systems and work with large datasets. It requires an understanding of programming languages, data analytics techniques and other technical skills.

Additionally, data engineers may experience tight deadlines and pressure from stakeholders to deliver results quickly.

What is the future of data engineering?

Data engineering is a rapidly growing field with no signs of slowing down. As more organizations rely on data-driven decisions, data engineers will continue to be in high demand. Data science and machine learning are becoming increasingly important, and data engineers can leverage these technologies to provide powerful insights into large datasets.

Final thoughts

A data engineer is a vital component within the technology and engineering sectors. Their skills and qualifications set them up to do great things in their careers by extracting data from various sources and turning it into useful insights for organizations.

The roles and responsibilities of a data engineer often require creativity, collaboration with stakeholders, and problem-solving abilities to perform complex tasks.

As more companies digitize their business activities, growth prospects for data engineers will continue to increase.