What Does a DataOps Engineer Do? Skills, Salary, and How to Become One

If you’re considering a career in DataOps, you’re looking at one of the most impactful roles in modern businesses. DataOps helps organizations improve the quality of their data, speed up decision-making, and enable seamless collaboration between teams. It empowers businesses to deliver accurate and timely data insights, manage data efficiently, cut costs, scale operations, and adapt to new challenges.

This article will guide you through what a DataOps engineer does, how this role compares to others in the data field, the salaries you can expect, and the steps you can take to start your journey toward this exciting career.

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Table of Contents

• What does a DataOps Engineer do? 
• Essential skills for a DataOps Engineer
• DataOps Engineer salary and job market trends
• How to become a DataOps Engineer
• Resources to get started as a DataOps Engineer
• Conclusion
  

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What Does a DataOps Engineer Do?

A DataOps engineer designs and builds data pipelines while continuously improving them through automation and teamwork. Their job is to make data accessible, reliable, and ready for use across different parts of a business.

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DataOps in Action: How DataOps Engineers Streamline Analytics

To give you an idea of what they do, let’s look at how they contribute to a machine learning project designed to predict customer churn. Here’s how a DataOps engineer would handle this:

1. Designing Data Pipelines for Collection and Ingestion

Your first task as a DataOps engineer is to set up systems that collect data from different sources. This includes sources like customer profiles, transaction logs, and support tickets. For instance, you might set up pipelines to move this raw data into a central location where it can be cleaned and prepared for analysis.

2. Integrating and Preparing Data

Once the data is collected, it needs to be cleaned and standardized. As a DataOps engineer, you would align customer IDs across systems, convert timestamps into a single format, and normalize transaction values. This step ensures the data is consistent and ready for machine learning.

3. Maintaining Data Quality

High-quality data is crucial for reliable results. You would create rules to detect issues like missing customer information, inconsistent churn labels, or invalid values like negative purchases. Catching these problems early helps prevent errors in the model.

4. Securing Data and Enforcing Compliance

Data security is a critical part of your role. For example, you might anonymize sensitive customer details, such as phone numbers, while keeping essential fields accessible for analysis. 

5. Automating and Scheduling Pipelines

Data workflows need to run smoothly without constant manual input. You would automate these processes, such as setting up nightly updates to process new data. 

6. Facilitating Feature Engineering

Feature engineering is where you add new data fields to improve model performance. As a DataOps engineer, you would work with data scientists to create features like "average spending over the last three months" or "time since the last support ticket" within the pipeline.

7. Testing and Maintaining Pipelines

Pipelines must work reliably. You would monitor for issues, such as database changes that break the flow of data, and fix them quickly to avoid disruptions in the project.

8. Scaling for Deployment

As the project grows, data pipelines need to handle larger volumes or process data in real time. For example, you might upgrade a batch pipeline to handle live data, enabling the system to predict churn immediately after customer interactions.

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Essential Skills for a DataOps Engineer

To succeed as a DataOps engineer, you’ll need a blend of technical expertise and soft skills. Here’s what you should focus on:

• Cloud Platforms: You should be familiar with cloud services like AWS, Azure, and Google Cloud for managing data storage and workflows.
• Data Pipeline Tools: Tools such as Apache Airflow and Kafka are essential for building and managing data pipelines.
• Programming: Strong knowledge of Python and SQL is critical for writing scripts and querying databases.
• Databases and Data Lakes: You need to understand relational databases like PostgreSQL and MySQL, non-relational databases like MongoDB and Cassandra, and data lakes.
• CI/CD Tools: Experience with Jenkins, GitLab CI/CD, or CircleCI is helpful for automating deployment processes.
• Infrastructure Management: Familiarity with tools like Terraform, Ansible, Docker, and Kubernetes allows you to containerize, orchestrate, automate and manage cloud infrastructure effectively.‍
• Collaboration and Communication: You will often work with data scientists, engineers, and business teams to understand their data needs and provide clear updates. For instance, explaining a pipeline issue to a non-technical team and its impact on deliverables is a key part of the role.‍
• Problem-Solving: Strong analytical thinking is essential to diagnose and resolve pipeline issues quickly. Whether it’s fixing missing records or addressing latency problems, your ability to find and implement solutions ensures smooth operations with minimal disruptions.‍
• Adaptability: Projects often change, requiring you to adjust workflows or adopt new tools. For example, you may need to transition from batch processing to real-time data ingestion to meet evolving machine learning requirements.

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DataOps Engineer Salary and Job Market Trends

According to ZipRecruiter, the average annual salary in the USA is $132,084, or $64 per hour, while in Canada it is $105,143 per year, or $50.55 per hour. These figures can vary based on factors like experience, location, and the industry you work in.

However, the demand for DataOps engineers is growing rapidly across industries such as finance, healthcare, and technology. Companies are looking for professionals who can streamline data pipelines, automate workflows, and improve data quality to support decision-making.

As more businesses rely on data-driven strategies, the job market for DataOps engineers is expected to continue expanding.

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How to Become a DataOps Engineer

Becoming a DataOps engineer requires a combination of education, practical experience, and specialized certifications. Here’s a step-by-step guide to help you get started:

Educational Background

A degree in Computer Science, Data Science, or Information Technology is a great foundation for this role. These programs provide essential knowledge in programming, data management, and system architecture, which are all critical for DataOps.

Career Path

Start with an entry-level role, such as a Data Analyst or Data Engineer, to build hands-on experience in handling and processing data. Focus on learning the basics of data pipelines, databases, and reporting tools. 

As you progress, expand your skill set by gaining expertise in areas like DevOps, automation tools, and data governance frameworks. Moreover, some basic knowledge of data science can also help in ML-related projects.

Certifications

Start with beginner-level certifications like DataOps Fundamentals for Beginners on Udemy and then pursue industry-recognized cloud certifications, such as:

• Azure Data Scientist Associate
• Google Professional Data Engineer
• AWS Certified Data Engineer
• AWS Certified DevOps Engineer – Professional
• Microsoft Certified DevOps Engineer Expert
• Professional Cloud DevOps Engineer

While these certifications are not specifically for DataOps, due to it being a relatively newer field, they cover core concepts like data science, engineering, and DevOps. These are essential for building a DataOps foundation and will accelerate your career as a DataOps Engineer. 

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Resources to Get Started as a DataOps Engineer

Starting your journey as a DataOps engineer requires both hands-on practice and structured learning. Begin by exploring open-source tools such as Apache Airflow, Docker, and Kubernetes. These platforms will help you understand the fundamentals of building and managing pipelines.

To deepen your knowledge, experiment with CI/CD pipelines using real-world datasets, which will provide practical insights into automating workflows.For structured learning, online courses on platforms like Coursera and Udemy offer comprehensive lessons tailored to both beginners and advanced learners. 

Also, supplement your studies with highly-rated books like Practical DataOps and DataOps: The Authoritative Edition, which covers key concepts in DataOps and related fields. To stay updated and solve practical challenges, join active community forums like Stack Overflow, Reddit, and GitHub. 

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Conclusion

A DataOps engineer plays a vital role in turning raw data into valuable insights. They are responsible for developing and maintaining robust, automated pipelines for various use cases across the organization.

The rising demand for businesses to be data-driven has made a career in DataOps both rewarding and increasingly sought after. This field has gained significant popularity among young graduates who are passionate about the data ecosystem, including areas like data engineering, data science, and pipeline management.

Many aspiring professionals pursue online DataOps courses and relevant certifications to enhance their skills and accelerate learning.Whether you’re starting with hands-on practice, pursuing certifications, or connecting with the community, DataOps offers excellent opportunities for growth and success.