What Is Data Engineering And What Does A Data Engineer Do? 

Interested in becoming a data engineer?

The need for data experts in the U.S. job market is expected to grow by 22% in this decade, and according to LinkedIn’s 2020 report, a data engineer is listed as the 8th fastest growing job today.

But what is data engineering exactly and what does a data engineer do?

We’ll explain what a data engineer is, what the job entails, and how to become a data engineer. Plus, we’ll explain how data engineers use Meltano, our DataOps platform, for efficient data management.  

What Is Data Engineering?

Data engineering is the process of designing systems for collecting, storing, and analyzing large volumes of data. Put simply, it is the process of making raw data usable and accessible to data scientists, business analysts, and other team members who rely on data.

Why Is Data Engineering Important?

Data engineering is important for collecting insights, which can be used to make informed business decisions.

Companies collect large volumes of data, but when raw, it is of no use to business analysts and decision-makers. This is where data engineers come in.

Data engineering helps make sense of large volumes of data by creating organized systems for collecting, managing, and storing data, in order for data scientists to access and analyze the data and derive valuable insights.

What Does A Data Engineer Do?

A data engineer is responsible for creating the environment as well as the processes used to collect, store, and manage data. A data engineer’s job is to essentially turn raw data into usable information.

Data engineers must be familiar with a wide array of tools and technologies, which are constantly evolving.

A data engineer’s focus is mainly on raw data, formats, security, and storage.

Their tasks include:

  • Designing systems for collecting and storing data
  • Testing various parts of the infrastructure to reduce errors and increase productivity
  • Integrating data platforms with relevant tools
  • Optimizing data pipelines
  • Using automation to streamline data management processes
  • Ensuring data security standards are met

When it comes to skills and tools data engineers must be familiar with, a typical data engineer job description includes:

  • Software engineering
  • Distributed systems
  • Open frameworks
  • System architecture
  • SQL
  • Python
  • Query Engines
  • Cloud platforms
  • ETL tools
  • Data modeling
  • Analytics
An image of people collaborating​
A data engineer creates the environment and processes used to collect, store, and process data ​

Data Scientist vs. Data Engineer: What’s The Difference?

While a data engineer builds and maintains the infrastructure for storing, organizing, and processing data, a data scientist analyzes the data to derive insights, make predictions and find answers to important questions businesses use to drive growth.

The field of data science is growing rapidly with new roles and technologies emerging constantly.

Companies are collecting more data than ever before, so data management, processing and storing is becoming more complex by the day.

A data scientist can no longer manage all data-related aspects, hence the appearance of data engineers.

Even though the data engineering roles and data scientist roles may overlap in some areas, each comes with its own distinct set of responsibilities.

How To Become A Data Engineer

To become a data engineer and work in one of the most in-demand fields today, you need the right set of skills. Since not many universities offer a data engineering degree, many data engineers have a degree in related fields such as computer science.

Aside from getting a degree in a related field, there are other steps you can take to become a data engineer, such as:

  • Learn to code and become proficient in programming languages such as SQL, Java, and Python
  • Understand cloud computing and cloud storage
  • Get a grasp of machine learning
  • Learn ETL/ELT to learn how to move data from data sources to data warehouses or databases
  • Learn how to write scripts to automate repetitive processes
  • Learn about databases and how they work
  • Learn about data security in order to securely manage data and protect it from loss
An image depicting a woman working on a computer​
To become a data engineer, you must acquire skills such as coding, cloud computing, and machine learning ​

How Data Engineers Use Meltano

The role of a data engineer is vital for companies that rely on data, as other data roles depend on them to build the systems for gathering, organizing, and maintaining data.

A data engineer must figure out how the data will be structured, test data pipelines, and keep an eye on the entire data management process.

However, to do their jobs well, data engineers require proper tools and solutions to facilitate the extraction of data from multiple sources.

Meltano is a DataOps platform that enables data engineers to streamline data management and keep all stages of data production in a single place.

Meltano is based on the extract, load, transform (ELT) principle to simplify the extraction of data from various sources and load it into data warehouses or databases, using Singer taps and targets.

With DevOps best practices applied to data workflow, data engineers can speed up the entire data management cycle by using automation, all while giving them control over their pipelines so any irregularities such as duplicate data are immediately spotted and fixed.

Data engineers can use Meltano for:

Data Extraction

Meltano uses Singer taps and targets for data replication and includes features such as schema validation and incremental syncs. It supports over 300 connectors, including Asana, Agile CRM, and 3PL Central for extracting data from any source and loading it into a warehouse. This can be any database, API, or file system that accepts Singer data.

Meltano’s built-in replication tool does 99% of the work for maximum efficiency.

Check out the video below to see Meltano ELT in action:

From 0 to ELT in 90 seconds with Meltano, tap-gitlab, and target-postgres

Data Transformation

Meltano enables data engineers to transform data using a structured query language (SQL) queries. The platform supports a data build tool (DBT) as a plugin, which is the standard in data transformations, enabling data engineers to create files containing statements and define how they want to transform their data.

They can run DBT in a scheduled workflow with other plugins such as Great Expectations or on its own, as needed.

Data Orchestration

Data orchestration allows businesses to streamline and automate the way they derive insights from data.

Data engineers can use Meltano to create scheduled pipelines and orchestrate them using Apache Airflow. This enables them to schedule and monitor data workflow in order to get real-time insights.


Automation is an essential factor in data management, as it helps save both time and money while increasing efficiency and reducing errors.

Meltano enables the automation of data delivery from various sources at the same time. With Meltano, data engineers can automate the delivery of structured and unstructured data, which significantly reduces development time.

Testing Data Quality

Untested and undocumented data can result in unstable data and pipeline debt. With Meltano, data engineers can run tests and check data quality.

Meltano supports integrating data pipelines with Great Expectations, which is the standard for checking data quality. Data engineers use this tool to ensure all data is validated, documented, and tested, and is therefore of high quality.


While data engineers don’t typically analyze data, they can prepare the data for analysis for data scientists and business analysts to access and derive insights.

Meltano supports Superset, an intelligence tool that enables data teams to connect the tool to data warehouses, create charts and visualize the data.

Wrapping Up On Data Engineering

Data engineering is the process of creating and maintaining systems for collecting, storing, and managing data. A data engineer’s role is to create the environments for extracting, organizing, and turning raw data into usable information.

A data engineer:

  • Designs systems for collecting and storing data
  • Tests the infrastructure to spot and reduce errors
  • Integrates data platforms with necessary tools
  • Optimizes data pipelines
  • Automates data management processes
  • Handles data security

A data engineer must have the following skills:

  • Software engineering
  • Distributed systems
  • Open frameworks
  • System architecture
  • SQL
  • Python
  • Query Engines
  • Cloud platforms
  • ETL tools
  • Data modeling
  • Analytics

Meltano enables data engineers to build data platforms integrated with all the necessary tools for streamlined collecting, storing, and processing of data.

Data engineers can use Meltano for:

  • Data extraction
  • Transformation
  • Orchestration
  • Automation
  • Testing data quality
  • Analysis

With Meltano, data engineers can create their ideal data platform, pull data from any source, automate repetitive processes, and manage everything from a single place.

They can also customize the platform by adding various plugins that Meltano supports and upgrade their platform with the latest tools and technologies.

Meltano offers the full toolset data engineers need to complete and manage their data platforms efficiently.

The best part? Meltano provides maximum flexibility, as it can be deployed anywhere.


You haven’t seen nothing yet!