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What Is A DataOps Engineer? Responsibilities + How A DataOps Platform Facilitates The Role  
Data and Analytics

What Is A DataOps Engineer? Responsibilities + How A DataOps Platform Facilitates The Role  

Data is becoming the world’s most valuable resource, according to an article in The Economist dating back to 2017. Since then, the way we compile, process, and store data has evolved significantly, and it continues to do so at incredible speed.

As more data becomes available, the demand for faster, improved, error-free analytics grows.

Who exactly is challenged with meeting this demand? DataOps engineers.

In this guide, we’ll explain all you need to know about a DataOps engineer, including their key responsibilities, and how these experts use a DataOps platform, like Meltano, to optimize internal processes.

What Is A DataOps Engineer?

A DataOps engineer creates the environment and the processes used to manage and store large volumes of compiled data.

Think about data operations as a factory assembly line where a warehouse engineer optimizes and automates processes to increase productivity and product quality. In the same way, a DataOps engineer designs the data assembly line that enables data scientists to derive insights from data analytics faster and with fewer errors.

How does this work?

DataOps engineers improve the speed and quality of the data development process by applying DevOps principles to data workflow, known as DataOps.

DataOps, which is based on Agile methodology and DevOps best practices, is focused on automating data flow across an organization and the entire data lifecycle, from aggregation to reporting.

The goal of DataOps is to speed up the process of deriving value from data. For this purpose, various parts of the data pipeline are automated to deliver analytics quickly and efficiently.

DataOps uses a wide range of technologies such as machine learning, artificial intelligence, and various data management tools to streamline data processing, testing, preparing, deploying, and monitoring.

This results in a system that gives organizations control over the data flow so that anomalies can be spotted automatically.

An image depicting a man working on a computer​
DataOps uses a range of technologies such as artificial intelligence and machine learning to streamline data processing, testing, deploying and monitoring ​

What Does a DataOps Engineer Do?

A DataOps engineer helps an organization operationalize its data by creating the environment and processes needed to efficiently manage data and derive value from analytics.

This includes various day-to-day activities, from reducing development time and improving data quality to providing guidance and support to data team members.

The responsibilities of a DataOps engineer include:

  • Building and optimizing data pipelines to facilitate the extraction of data from multiple sources and load it into data warehouses. A DataOps engineer must be familiar with extract, load, transform (ELT) and extract, transform, load (ETL) tools.
  • Using automation to streamline data processing. To reduce development time and increase data reliability, DataOps engineers automate manual processes, such as data extraction and testing.
  • Managing the production of data pipelines. A DataOps engineer provides organizations with access to structured datasets and analytics they will further analyze and derive insights from.
  • Designing data engineering assets. This involves developing frameworks to support an organization’s data demands.
  • Facilitating collaboration. DataOps engineers communicate and collaborate with other data and BI team members to enhance the quality of data products.
  • Testing. This involves executing automated testing at every stage of a pipeline to increase productivity while reducing errors. This includes unit tests (testing separate components of a data pipeline) as well as performance tests (testing the responsiveness) and end-to-end tests (testing the whole pipeline).
  • Adopting new solutions. This includes testing and adopting solutions and tools that adhere to the DataOps best practices.
  • Handling security. DataOps engineers ensure data security standards are applied across the data pipelines.
  • Reducing waste and improving data flow. This involves continually striving to reduce wasted effort, identify gaps and correct them, and improve data development and deployment processes.

DataOps Engineer vs. Data Engineer

While a data engineer builds systems and pipelines to turn raw data into usable information, a DataOps engineer focuses primarily on streamlining the development process.

While definitions of each of these roles may vary across organizations, each is responsible for making data available to data analysts, scientists, and other team members who depend on it.

The two roles are quite similar, with one exception: A DataOps engineer, which is a relatively new term, designs and supports the underlying architecture for data processing by applying DataOps best practices, unlike a data engineer in general, who may or may not use DataOps for this purpose.

An image of a person observing data analytics​
A DataOps engineer designs the underlying architecture for data processing by applying DataOps best practcies​

How Meltano, A DataOps Platform, Supports DataOps Engineers

DataOps engineers are skilled in applying DataOps principles and selecting adequate tools to optimize data workflow. However, the lack of coordination and the complexity of the modern data stack can often be a challenge, leading to delays, which increase development time.

Meltano, a DataOps platform, helps engineers solve these challenges by providing a single place to manage all aspects of data operations. It allows engineers to install and configure any tools they find necessary for their data platform.

DataOps engineers can use Meltano for:

Data Replication

Meltano lets engineers easily extract data and load it into their databases using Singer taps and targets.

Meltano has an ever-growing connector library, supporting 300+ Singer taps that allow users to extract data from various sources. Some of the extractors Meltano supports include 3LP Central, AdRoll, and Agile CRM.

Data can be loaded into arbitrary destinations such as a database, API, or file system that accepts Singer data.

Meltano’s built-in data replication feature does 99% of the work, significantly reducing the time needed to extract and load data into different databases.


Meltano facilitates data transformation by supporting the plugin for the data build tool (DBT), which is the standard in SQL. This allows engineers to define how data will be transformed in their warehouse, using version-controlled SQL.

With DBT, engineers simply have to create models — single files that contain a statement used to transform raw data into tables. Meltano also provides the option to run DBT in a scheduled workflow or by itself, depending on the organization’s needs.


Data orchestration is essential to derive value from data. It allows organizations to control, schedule, and monitor data to get real-time insights.

DataOps engineers can use Meltano to orchestrate scheduled pipelines using Apache Airflow. This allows engineers to author, schedule, and monitor workflows. With Meltano, processes like taking raw data from multiple sources, combining it, and making it available to data analytics tools are automated.


Since automation is key when it comes to the role of a DataOps engineer, it is important to select a data platform that supports the automation of data delivery from multiple sources simultaneously — like Meltano.

Meltano allows engineers to automate the delivery of both structured and unstructured data in hybrid and cloud environments. This helps reduce data development time while increasing accuracy.

Data Quality

Meltano helps DataOps engineers improve and maintain data quality by supporting integration with Great Expectations, an open standard for checking data quality. By bringing this solution into the data pipeline, data is automatically validated, tested, documented, and profiled.

This helps engineers maintain data quality at all times, therefore eliminating pipeline debt, which occurs when data is untested, undocumented and as a result, unstable.

Data Analysis 

Once data is extracted, transformed, and cleaned, it is ready to be analyzed to derive usable insights.

Meltano facilitates data analysis by supporting intelligence tools such as Superset. Superset is an open-source data visualization platform on which data engineers can create charts and dashboards to visualize their data.

This tool can easily be installed and configured and used to connect your project to various data warehouses for data visualization.

The Role Of A DataOps Engineer — Key Takeaways

DataOps engineers create the environment and the processes used to manage and store large volumes of data.

These data professionals:

  • Build and optimize data pipelines
  • Streamline data processing
  • Manage the production of data pipelines
  • Design data engineering assets
  • Facilitate collaboration and communication
  • Execute automated testing
  • Adopt new solutions
  • Apply data security standards
  • Constantly seek ways to reduce waste and improve data flow

Meltano allows DataOps engineers to perform these tasks by facilitating:

  • Data replication
  • Transformation
  • Orchestration
  • Automation
  • Data quality
  • Data analysis

Meltano allows data engineers to build and manage every aspect of their data platform from a single place, while constantly improving it by deploying and managing new tools with a single control plane.

The best part? Meltano provides a comprehensive list of plugins to choose from, which can be added and upgraded at any time, allowing DataOps engineers to stay up to date with the latest tools and technology.

Meltano allows engineers to craft their ideal end-to-end data platform using their preferred data tools and technologies. In addition to a cohesive, streamlined experience, Meltano also provides the ultimate flexibility, as it can be deployed anywhere.

Own your data stack, end to end, with Meltano!

Category: Data and Analytics

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