Data engineers are the ones tasked with carrying out data engineering tasks. They usually have a background in IT or computer science and help manage the raw data to make it accessible and usable. Data engineers are specialists who extract value from raw and unstructured data and move it around without changing it.

Dataquest describes 3 categories of data engineer

Dataquest describes 3 categories of data engineer

1. Generalist

Generalists are usually found in small teams or small companies and are often the only data-focused person on the team. Their work can include data management, analysis, and more. This is a good transition role for those who want to change from data science to data engineering. A small business doesn’t have to worry about engineering “for scale” because it has less volume.

2. Pipeline-centric:

Pipeline-centric data engineers are found in mid-sized companies and work with data scientists. This role requires in-depth knowledge of distributed systems, computer science, and software engineering.

3. Database-centric:

Larger organizations often have data engineers to organize the flow of data. Database-focused data engineers focus on working with different databases and maintaining relations between them.

Having the right skills & qualifications is important to be considered a data engineer. For instance:

Apache Hadoop and Apache Spark: Hadoop, which is the Apache open-source framework for processing large data sets across clusters of computers, provides you with a simple way to use programming models to process vast amounts of data. The software can reach from one server to many servers: Python, Scala, Java and R are some of the languages that the platform supports.

C++: C++ is a general-purpose language that makes data management and maintenance easier. It also provides real-time analytics, making it a powerful tool.

Database systems (SQL and NoSQL): SQL is the most widely used programming language when it comes to database management. It also helps build and manage relational database systems, which are composed of tables (rows and columns). When there are no tables, you have Non-tabular databases.

4. Data warehouse:

Data warehouses are a place to store data that’s been collected and stored over time. It can be current or any specific point in history, and collects data from different sources such as your CRM, accounting software, and ERP. Businesses use this data for reporting, analytics, and data mining, among other business-related tasks. Data engineers are expected to be familiar with Amazon Web Services or AWS, various data storage software and cloud services platform in order to perform the tasks mentioned above.

5. ETL (Extract, Transfer, Load) tools:

ETL is the process of extracting data from one source, transforming it into a readable format and then storing it in a data warehouse for analysis. Batch processing is necessary to analyze query-able data that adheres to particular business interests. ETL extracts data from different sources, then performs a series of operations (according to set parameters) to convert the data before it’s loaded into a database or business intelligence platform.

6. Machine learning:

We use algorithms in the process of machine learning and these are also called models. Data scientists are able to predict scenarios by using these models. Data engineers are expected to know about machine learning as it would help them work with data scientists. Data pipelines are built and developed by data engineers who put their effort into ensuring that these pipelines are more accurate. They do this with the help of models.

7. Data APIs:

The interface software companies develop to provide access to your data is called an API. It makes it possible for multiple machines & applications to talk with each other and share their data, which can make it a lot easier for us to complete tasks we would otherwise need a human for doing. For example, web apps that use APIs can be built with a front-end and back-end. The front-end who communicates with the user will communicate to the back-ends data & abilities in order to give a better user experience.

8. Programming languages- Python and Java:

Stats for Python is the most popular programming language for data modeling and analysis. The Java programming language is used in creating many data architecture frameworks with lots of APIs to assist your work as a Java developer.

9. Basic understanding of the distributed systems:

Data Engineers are expected to have a solid understanding of Apache Hadoop. The AWS software library presents a framework that permits the distribution of data analysis tasks out to clusters of computers with simplified programming models. Apache Spark is designed to scale from small machines to thousands of machine clusters across the world. As a general computing framework, it’s got various tools and libraries which makes programming in data science quite easy. Scala is one of the most popular languages for use with Apache Spark.

10. Knowledge of algorithms and data structures:

A data engineer has a lot of responsibilities. Not only is it your job to filter and optimize data, you also need to know algorithm design in order to understand how the rest of the company’s data are working. Beyond this, other things that come with it are checkpoints and end goals. These will make it easier to solve traffic-related obstacles.

Responsibilities in data engineering

Data engineering comes with particular responsibilities daily.

  • Designing architectural designs: working on and/or implementing, testing and maintaining large-scale architectures like databases. Ensuring that your design supports the business requirements.
  • Developing data set processes for data modeling, production and mining.
  • Finding ways for you to improve data reliability, efficiency and quality.
  • Utilizing a range of analytical tools we can prepare the data necessary to create models. We work with large volumes of data both from internal and external sources. Researching business-related questions are also a large part of our job.

Role of data engineers - Chapter247