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Dataops Jobs in California (NOW HIRING)

Marencor

AWS Data Engineer

San Francisco, CA · On-site

$134K - $162K/yr

Familiarity with DataOps concepts and tooling for source control and setting up CI/CD pipelines on AWS. * Hands-on experience with Databricks and a willingness to grow capabilities. * Experience with ...

Stealth Social Media Startup

Senior Data Engineer

San Jose, CA · On-site

$124K - $168K/yr

Azure experience strongly preferred • Strong communication skills -- you work cross-functionally and can explain complex systems clearly Preferred : • DataOps experience -- ability to own data ...

Oclaro

Senior Manager, Data Engineering

San Jose, CA · On-site

$134K - $192K/yr

Establish CI/CD pipelines and DataOps best practices for automated and secure deployment * Implement data governance standards including cataloging, lineage, and metadata management * Design strong ...

NextData

Senior Sales Engineer

San Francisco, CA · On-site

Experience with AI/ML platforms , MLOps, DataOps or developer platforms * Cloud and data engineering certifications * Public speaking or thought leadership experience in data infrastructure or ...

Socket.dev

Data Platform Engineer

San Francisco, CA · On-site

$200 - $250/hr

Astronomer empowers data teams to bring mission-critical software, analytics, and AI to life and is the company behind Astro, the industry-leading unified DataOps platform powered by Apache Airflow ...

Vast

Senior Data Engineer

Long Beach, CA · On-site

$143K - $203K/yr

Knowledge of DevOps/DataOps practices including CI/CD, infrastructure as code (Terraform, CloudFormation), and containerization (Docker, Kubernetes) * Experience with real-time streaming ...

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How much do dataops jobs pay per hour?

As of Sep 7, 2026, the average hourly pay for dataops in California is $22.83, according to ZipRecruiter salary data. Most workers in this role earn between $17.31 and $23.70 per hour, depending on experience, location, and employer.

What is a 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.

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.

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.

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 popular job titles related to Dataops jobs in California?

For Dataops jobs in California, the most frequently searched job titles are:

What job categories do people searching Dataops jobs in California look for?

The top searched job categories for Dataops jobs in California are:

What cities in California are hiring for Dataops jobs?

Cities in California with the most Dataops job openings:

Dataops jobs near you
Infographic showing various Dataops job openings in California as of August 2026, with employment types broken down into 88% Full Time, 6% Part Time, and 6% Contract. Highlights an 68% Physical, 11% Hybrid, and 21% Remote job distribution, with an average salary of $47,480 per year, or $22.8 per hour.

Lead Azure data engineer

Omega Solutions, Inc.

Santa Clara, CA • On-site

$120K - $158K/yr

Full-time

Re-posted 5 days ago

Job description

Job Summary:
Omega Solutions, Inc. is seeking a Lead Azure Data Engineer to design, develop, test, and deploy data engineering pipelines on the Azure cloud platform. The role involves providing technical leadership, analyzing data quality issues, and participating in design and code reviews.
Responsibilities:
• Design, develop, test, and deploy Data Engineering pipelines on Azure cloud platform.
• Provide technical leadership and guide the team.
• Analyze and profile the data to understand and resolve the data quality issues.
• Participate in design and code reviews and provide guidance.
• Create near real-time data pipelines and document the design.
Qualifications:
Required:
• 10+ years of overall IT experience in designing and implementing Data Engineering solutions.
• 6+ years of experience primarily on Azure cloud platform using Azure BLOB Storage / ADLS, Azure Data Factory, Databricks, Synapse, Logic Apps and Key Vault.
• Must have worked as a Lead on at least 2 projects.
• Experience in coding SQLs and Python.
• Must have experience in Data Warehousing.
Preferred:
• Azure Cloud certification preferred.
• Informatica Cloud experience is a plus.
• Experience in Cosmos, HDInsight, Event Hubs, Azure Functions, APIs is a plus.
• Experience in streaming data ingestion and stream data processing is a plus.
• Experience in Spark (Python, Scala, or SQL) is a plus.
• DataOps, or DevOps experience is a plus.
Company:
Omega was incorporated in 2007 in the State of California to offer high end IT Solutions ranging from IT Software and product development to technology deployment and specialize in providing software solutions to diverse business sectors in USA and World-wide. Founded in 2007, the company is headquartered in Santa Clara, USA, with a team of 11-50 employees. The company is currently Early Stage.

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