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Dataops Jobs (NOW HIRING)

Analytica

Senior Data Engineer

Washington, DC · On-site

$119K - $162K/yr

The ideal candidate possesses deep expertise in cloud-native data engineering, DataOps practices, and regulated healthcare environments. The Senior Data Engineer will lead the development and ...

New

Peloton

Data Engineering Manager

New York, NY · On-site

$173K - $213K/yr

Lead the DataOps function within the Data Engineering team, driving operational maturity, platform reliability, and process improvements * Manage and mentor offshore engineering resources, providing ...

Analytica

Senior Data Engineer

Washington, DC · On-site

$120K - $163K/yr

The ideal candidate possesses deep expertise in cloud-native data engineering, DataOps practices, and regulated healthcare environments. The Senior Data Engineer will lead the development and ...

New

Tech Tammina

DBT Engineer (GCP)

$117K - $140K/yr

Strong Git workflows, CI/CD, and DataOps practices * Proven experience in optimizing and modeling data pipelines on GCP

1 point system

Data Engineer

Dallas, TX · On-site

$113K - $136K/yr

You will act as a "Full Stack" data professional, handling everything from infrastructure automation (DataOps) to complex nested data modeling. Key Responsibilities * Real-Time Ingestion: Build ...

Flight Centre Travel Group (usa) Inc

$97K - $117K/yr

Embed DataOps practices, including pipeline observability, automated testing, CI/CD for data, and the reliability of data products * Provide technical design and best-practice guidance for data, AI ...

Tata Consultancy Services

Data Engineer

Plano, TX · On-site

$110K - $132K/yr

... and DataOps practices. Qualifications : Required : • Snowflake • DBT • Design and build data pipelines and models using Snowflake and dbt • Lead a team, provide technical guidance, and ...

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$12

$23

$36

How much do dataops jobs pay per hour?

As of Jul 15, 2026, the average hourly pay for dataops in the United States is $23.13, according to ZipRecruiter salary data. Most workers in this role earn between $17.55 and $24.04 per hour, depending on experience, location, and employer.

What are DataOps?

DataOps, short for Data Operations, is a set of practices, processes, and technologies that combine data engineering, data integration, and DevOps methodologies to improve the quality and speed of data analytics. DataOps aims to streamline the flow of data from source to value, enabling organizations to deliver reliable, high-quality data to stakeholders more efficiently. This approach emphasizes collaboration, automation, and monitoring throughout the data lifecycle to reduce errors and shorten development cycles. The ultimate goal of DataOps is to create an agile data pipeline that adapts quickly to changing business needs.

What is the difference between Dataops vs Data Engineer?

AspectDataopsData Engineer
Primary FocusAutomating data workflows, deployment, and operational efficiencyBuilding and maintaining data pipelines, storage, and infrastructure
Skills & CertificationsDevOps tools, scripting, cloud platforms, CI/CD practicesSQL, ETL tools, cloud platforms, programming (Python, Scala)
Work EnvironmentCollaborates with DevOps, data teams, and operationsWorks closely with data scientists, analysts, and infrastructure teams
Industry UsageUsed in organizations focusing on data deployment and automationUsed in data infrastructure development and data pipeline creation

While both Dataops and Data Engineers work with data infrastructure, Dataops emphasizes automation, deployment, and operational efficiency, whereas Data Engineers focus on building and maintaining data pipelines and storage systems. Understanding these differences helps organizations assign the right roles for their data needs.

What are the key skills and qualifications needed to thrive as a DataOps Engineer, and why are they important?

To thrive as a DataOps Engineer, you need expertise in data engineering, automation, cloud platforms, and a solid understanding of CI/CD pipelines, typically backed by a degree in computer science or related fields. Familiarity with tools like Apache Airflow, Kubernetes, Docker, Jenkins, and cloud services such as AWS, GCP, or Azure is commonly required, along with knowledge of scripting languages like Python or Bash. Strong collaboration, problem-solving, and communication skills help DataOps professionals work effectively across data, development, and operations teams. These abilities ensure reliable, scalable, and efficient data infrastructure, enabling organizations to quickly deliver high-quality data solutions.

How does a DataOps professional typically collaborate with data engineers, analysts, and other IT teams?

DataOps professionals play a key role in bridging the gap between data engineering, analytics, and IT by facilitating efficient, automated workflows and ensuring data quality across the pipeline. They often work closely with data engineers to streamline data integration and deployment processes, while collaborating with analysts to support timely access to reliable data. Regular communication and cross-functional teamwork are essential, as DataOps is responsible for implementing best practices that help different teams deliver insights faster and with fewer errors. This collaborative environment also encourages continuous feedback and process improvement.
More about Dataops jobs
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Dataops jobs near you
Azure Data Engineer

Azure Data Engineer

West Advanced Technologies (WATI)

Downey, CA • On-site

$117K - $140K/yr

Full-time

Re-posted 13 days ago

Job description

Job Title: (Azure Data Engineer) with Lakehouse Platform
Location: Downey, CA, 90242 :: Remote
Duration: 12 Months
Skills Required
Possess knowledge and technical expertise in standards and technologies to support complex business analysis, solution selection, systems design, and application integration - SQL and Relational Databases such as Oracle, SQL Server - Designing & developing SQL queries, stored procedures, views, debugging & tuning complex queries for optimal performance - UNIX shell scripts - Python - Azure Cloud - Azure Data Factory - Databricks - DataOps.
This classification must have a minimum of
seven (7) years of applying Enterprise Architecture principles.
At least five (5) years of that experience must be in a lead capacity. Q
5 years of experience in Azure Data Factory
5 years of experience in Databricks
5 years of experience in Managing Azure Resources
5 years of experience in automating Azure Data Resources using DataOps
5 years of experience in developing data models and data pipelines using Python
5 years of experience in Lakehouse Platform.
Education:
This classification requires the possession of a bachelor's degree in an IT-related or Engineering field. Additional qualifying experience may be substituted for the required education on a year-for-year basis.

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