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Dataops Jobs in California (NOW HIRING)

West Advanced Technologies

Azure Data Engineer

Downey, CA · On-site

$118K - $142K/yr

... 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 ...

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 ...

GSK

Senior Gen AI Platform Engineer

San Francisco, CA · On-site

$123K - $169K/yr

... DataOps, and Data Engineering products for best performance and ease of use in ML training at scale • Participate in scrum teams and contribute technical expertise to teams in closely aligned ...

Kai

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 ...

Judge Group, Inc.

Data Engineer III

Newport Beach, CA · On-site

$65 - $79/hr

Experience leading DataOps/DevOps functions including release, deployment, operations, and monitoring of data products. * 1+ years of experience with Snowflake, dbt, and Matillion. * Experience ...

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How much do dataops jobs pay per hour?

As of Jul 4, 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 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.
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 June 2026, with employment types broken down into 81% Full Time, and 19% Contract. Highlights an 74% In-person, and 26% Remote job distribution, with an average salary of $47,480 per year, or $22.8 per hour.

DataOps Analyst

Horizon Surgical Systems, Inc.

Los Angeles, CA • On-site

Full-time

PTO

Posted 16 days ago

Job description

Open Position - DataOps Analyst
Horizon Surgical Systems Inc.
Horizon Surgical Systems Inc. is revolutionizing the world of surgical ophthalmology by developing a novel, AI driven, and imaging-guided surgical robotic system. Horizon Surgical Systems Inc. aims to expand access to care, provide superior capabilities to the human surgeon, and enhance patient outcomes. Microsurgery in general and Ophthalmology are subfields of surgery for which the surgical outcomes can be significantly improved by robotic systems to allow superior dexterity, precision, accuracy, and visualization beyond the human surgeon's own capabilities.
We are seeking highly motivated, and intellectually inquisitive individuals looking to make a positive impact on healthcare via the development of robotic technology. The core values of Horizon Surgical Systems Inc. are:
  • Commitment to Excellence: We aim to deliver superior patient outcomes and surgeon experiences
  • Passion for Creativity and Innovation: We are driven by new ideas and aim to push the boundaries of what's possible
  • Teamwork and Camaraderie: We achieve our best when we collaborate and work together
  • Welcoming of Critical Opinion: We are enriched by constructive criticism and support the best ideas
  • Personal Accountability: We honor our commitments and take responsibility for our actions

Horizon Surgical Systems Inc. offers:
  • An opportunity to build autonomous surgical robotic systems driven by image guidance and AI technology for the future of affordable, high-quality healthcare.
  • The opportunity to work alongside clinicians, engineers, and global leaders in cutting-edge AI, imaging, and robotics technology.
  • Competitive compensation and an excellent company-paid benefits package.

Role:
We're looking for a sharp, curious DataOps Analyst to join the small but mighty data team at Horizon Surgical Systems. You'll be on-site in Santa Monica, working closely with our principal DataOps engineer - someone with 10+ years of experience in data pipelines who'll be invested in your growth. This is a hands-on role where you'll move and manage surgical data between Polaris, our cataract surgery platform, and our cloud environment where custom AI model training happens. If you're early in your career and want to do real, meaningful work in medical AI, this is a great place to grow.
WHAT YOU'LL BE DOING
  • Move and manage data between Polaris (our cataract surgery platform) and our AWS cloud training environment, keeping pipelines healthy and well-documented.
  • Build and maintain ETL/ELT workflows using Python and Bash, and help us make them more reliable over time.
  • Monitor pipeline health, catch anomalies early, and troubleshoot failures before they become problems.
  • Maintain data catalogs, versioning, and audit trails so our datasets stay traceable and well-organized.
  • Collaborate with our AI/ML engineers to curate, version, and validate training and test datasets.
  • Write scripts and automation tools that reduce manual data handling - Linux/Bash fluency matters here.
  • Support data requests from cross-functional teams including Regulatory, Clinical, and Systems Engineering.
  • Generate dashboards and reports on pipeline health, data quality, and throughput metrics.

WHAT WE'RE LOOKING FOR
  • Bachelor's degree in Computer Science, Data Science, or a related field - or equivalent hands-on experience.
  • 2+ years of experience in a data operations, data engineering, or data analysis role.
  • Proficiency in Python for data manipulation, scripting, and automation.
  • Solid understanding of the AWS ecosystem (S3, Lambda, Glue, or similar services).
  • Comfortable working in Linux environments and writing Bash scripts.
  • Git-fluent: version control is part of your daily workflow, not an afterthought.
  • You take data quality seriously and document your work clearly.
  • Good communicator - you can work with technical and non-technical stakeholders without losing them.

BONUS POINTS IF YOU HAVE
  • Experience in a medical, clinical, or regulated environment (FDA, ISO 13485, or similar).
  • Background in image processing or computer vision workflows.
  • Knowledge of statistics - data distributions, sampling methods, quality metrics.
  • Familiarity with DICOM or ophthalmic imaging data (OCT, surgical microscopy).
  • Exposure to ML data lifecycle concepts: dataset versioning, data drift, model validation sets.

WHAT TO EXPECT
You'll be mentored directly by our principal DataOps engineer, who has 10 years of experience building and running production data pipelines. This isn't a role where you'll be thrown in the deep end alone - you'll have real guidance and support as you ramp up. That said, we're a startup, so the work is real from day one and the pace is honest.
This is an exciting opportunity to join a high-tech startup that is poised to revolutionize surgical robotics in ophthalmology.
The base salary range for this role is $100,000-$115,000, in addition to a performance-based annual bonus, equity (stock options), a comprehensive benefits package, and a generous PTO policy.

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