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Dataops Jobs in California (NOW HIRING)

Astronomer

Staff Product Manager - SF

San Francisco, CA · On-site

$240K - $270K/yr

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 ...

Astronomer

Staff Product Manager - SF

San Francisco, CA · Remote

$270K/yr

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 ...

Revolution Medicines

Senior Data Engineer

Redwood City, CA · On-site

$129K - $176K/yr

Help establish DataOps practices that allow analytics, AI, ML, and business intelligence use cases to move safely from prototype to production. Cross-Functional Collaboration * Partner heavily with ...

Dlapiper

Data Engineer

Los Angeles, CA

$123K - $148K/yr

... DataOps concepts and operating in cross-functional teams that include data engineering personas. * The measures of success for this role include delivering data pipelines with trusted, quality data ...

World Market

Lead Data Engineer

Alameda, CA · On-site

$155K - $175K/yr

Own source code management, documentation (technical and end-user), and release planning for data engineering products; lean into DataOps, DevOps, and CI/CD to deliver reliable, tested, and scalable ...

The Marlin Alliance

Data Engineer

San Diego, CA · On-site

$122K - $147K/yr

Experience with DataOps or similar mission-critical pipeline automation (e.g., deduplication, schema normalization). * Any experience leveraging universal data distribution architectures within DOD ...

Vast

Staff Data Architect

Long Beach, CA · On-site

$67 - $86.25/hr

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

Dlapiper

Data Engineer

Palo Alto, CA

$134K - $161K/yr

... DataOps concepts and operating in cross-functional teams that include data engineering personas. * The measures of success for this role include delivering data pipelines with trusted, quality data ...

Acceldata

VP of Enterprise Sales

Campbell, CA · On-site

... DataOps. Delivered as a SaaS solution, Acceldata is trusted by leading global organizations such as HPE, HSBC, Visa, Freddie Mac, Manulife, Workday, Oracle, PubMatic, PhonePe (Walmart), Hershey ...

Hive

Data Operations Analyst

San Francisco, CA · On-site

$60K - $85K/yr

At Hive, our DataOps team is responsible for supporting the development of our proprietary AI models leveraging our Hive Data platform to deliver high-quality training and testing datasets. Day to ...

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Dataops information

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How much do dataops jobs pay per hour?

As of Aug 15, 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.

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.

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 100% Contract. Highlights an 100% In-person job distribution, with an average salary of $47,480 per year, or $22.8 per hour.

Data Engineer, Principal (Hybrid)

Releady

San Francisco, CA • Hybrid

$180K - $210K/yr

Contractor

Posted 24 days ago

Job description

OVERVIEW

Releady is partnering with a leading healthcare technology company to hire a Data Engineer, Principal for its Data Services team. This organization provides the technology backbone and shared data infrastructure for nonprofit, community, and regional health plans nationwide, unifying clinical, claims, demographic, and provider data into a single governed platform that powers automation and AI deployment across core health plan operations.

This role reports to a Senior Manager, Data Solutions, or Director, and partners with Enterprise Architects, Portfolio, Analytics, and Data Engineering teams to design technical solutions and build data products that meet enterprise-wide data needs. The Principal drives data product delivery by designing and implementing cloud data lakes, data warehouse, and data mart solutions, and is expected to influence enterprise architecture decisions and mentor senior engineering talent.

NOTE: *Must be eligible to work on W2 without sponsorship. Not eligible for C2C.

Employment Type: Contract-to-Hire (six-month contract term)

Location: Hybrid — 2x/week onsite; San Diego, Long Beach, Sacramento, Rancho Cordova, or Oakland (SF Bay Area)

Compensation: $85 – $95/hr

RESPONSIBILITIES

Data Platform Architecture & Delivery

Lead the design, development, and implementation of scalable data pipelines supporting enterprise data lakes, data warehouses, and data marts.

Engineer robust ELT/ETL solutions that ingest, process, and curate structured and semi-structured data from diverse internal and external sources.

Apply advanced data modeling techniques, including Data Vault 2.0, dimensional, and domain-oriented models, to support analytics and data products.

Cross-Functional Solution Design

Partner with Solution Design, Architecture, and Product teams to ensure technical designs are implemented accurately, efficiently, and securely.

Build and optimize data solutions on cloud platforms such as Snowflake, Databricks, and Synapse, with a focus on performance, scalability, reliability, and cost efficiency.

Quality, Governance & Operations

Implement data quality, validation, observability, lineage, and governance controls embedded directly into data pipelines.

Champion and apply DevOps and DataOps best practices, including CI/CD, automated testing, infrastructure as code, monitoring, and alerting.

Identify performance bottlenecks, reliability risks, and optimization opportunities across data platforms and workflows.

Technical Leadership & AI Enablement

Provide hands-on technical leadership and mentorship to senior and mid-level data engineers, promoting engineering standards and best practices.

Collaborate using agile methodologies to plan work, refine technical stories, and deliver iteratively with predictable outcomes.

Support integration of AI/ML-ready data assets, ensuring data is trustworthy, well-modeled, and accessible for advanced analytics use cases.

Act as a technical thought leader, advocating for modern data engineering patterns, tools, and practices aligned to enterprise strategy.

QUALIFICATIONS

Bachelor's degree or equivalent experience, with a minimum of ten years of relevant data engineering experience.

Experience supporting enterprise AI/ML, advanced analytics, and data product ecosystems.

Expertise in Data Vault 2.0, dimensional modeling, Lakehouse architecture, or domain-driven data design.

Demonstrated ability to influence enterprise architecture decisions and mentor senior engineering talent.

Expert-level understanding of data management practices, including data modeling (Data Vault 2.0), master data management, data integration, architecture, virtualization, warehousing, privacy, and security.

Demonstrated enthusiasm for AI and emerging technologies, with hands-on experience applying AI-driven solutions in enterprise environments.

Hands-on experience with SQL, Python, DBT Cloud, and DBT Core; expert-level fluency in NoSQL and database platforms such as Snowflake, Synapse, or Databricks.

Proven ability to design and scale large-volume, high-performance data platforms leveraging parallel and distributed architectures.

Advanced knowledge of CI/CD, source control, and agile delivery tooling such as Git, Bitbucket, and Jira.

Air Experience with Airflow or Tidal (migrating from Tidal; comparable orchestration tools considered)

Experience in healthcare, regulated environments, or large enterprises is strongly preferred.

We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, disability status, or other non-merit factor. We are committed to creating a diverse and inclusive environment for all employees.