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Dataops Engineer Jobs in California (NOW HIRING)

Worldmarket

Lead Data Engineer

Alameda, CA · Remote

$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 ...

Marencor

AWS Data Engineer

San Francisco, CA · On-site

$134K - $162K/yr

Role: AWS Data Engineer (Python, Dataiku) Location: SFO, CA - Hybrid Job Brief As an AWS Data ... Familiarity with DataOps concepts and tooling for source control and setting up CI/CD pipelines on ...

The Marlin Alliance, Inc.

Data Engineer

San Diego, CA · On-site

$121K - $146K/yr

They are seeking a Data Engineer to provide client support to their Navy client, focusing on ... DoDAF, CADM). • Experience with DataOps or similar mission-critical pipeline automation (e.g ...

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

... DataOps platform powered by Apache Airflow ® . Astro accelerates building reliable data products ... As a Data Platform Engineer at Astronomer, you'll be a key partner to our clients, guiding them in ...

Dlapiper

Data Engineer

Los Angeles, CA · On-site

$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 ...

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Dataops Engineer information

What is a DataOps engineer?

A DataOps Engineer is responsible for streamlining and automating data workflows, ensuring data quality, and enabling efficient data integration across platforms. They work closely with data scientists, analysts, and engineers to implement CI/CD pipelines, manage data infrastructure, and optimize data delivery processes. Their role involves leveraging tools for orchestration, monitoring, and version control to enhance collaboration and reliability in data operations.

What are the common day-to-day responsibilities of a DataOps engineer?

A Dataops Engineer is typically responsible for designing, deploying, and maintaining automated data pipelines that support business analytics and operations. Daily tasks often include monitoring data workflows, troubleshooting pipeline issues, optimizing system performance, and collaborating with data scientists, analysts, and DevOps teams to ensure seamless data delivery. You may also be involved in implementing data quality checks, managing cloud resources, and improving deployment processes. This role is dynamic and fast-paced, requiring both technical expertise and effective cross-team communication. Working as a Dataops Engineer provides the opportunity to work on cutting-edge projects and directly influence data-driven decision-making across the organization.

What are the key skills and qualifications needed to thrive in the DataOps engineer position, and why are they important?

To thrive as a Dataops Engineer, you need a strong background in data engineering, automation, CI/CD practices, and cloud platforms, typically supported by a degree in computer science or a related field. Familiarity with tools like Jenkins, Docker, Kubernetes, Terraform, and major cloud providers (AWS, Azure, GCP) as well as relevant certifications significantly enhances effectiveness in this role. Strong problem-solving skills, collaboration, and clear communication are essential soft skills for working across teams and addressing fast-changing data needs. These combined abilities ensure smooth data pipeline operations, minimize downtime, and enable efficient, reliable delivery of data-driven solutions.

What are the most commonly searched types of Dataops Engineer jobs in California?

The most popular types of Dataops Engineer jobs in California are:

What are popular job titles related to Dataops Engineer jobs in California?

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

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

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

Dataops Engineer jobs near you
Infographic showing various Dataops Engineer job openings in California as of August 2026, with employment types broken down into 95% Full Time, 2% Part Time, and 3% Contract. Highlights an 85% Physical, 6% Hybrid, and 9% Remote job distribution.

AI Engineer in Synthetic Data

Logical Intelligence

San Francisco, CA • On-site

$134K - $162K/yr

Full-time

Posted 12 days ago

Job description

Who we are

At Logical Intelligence, we're revolutionizing software development with AI-powered formal verification. We've developed groundbreaking agents that provide mathematical guarantees of code correctness, ensuring that software behaves exactly as intended while proactively identifying bugs and security vulnerabilities. Our novel foundation model enables scalable, precise reasoning for formally verifiable code across Rust, Golang, and smart contract VMs. We've won ​​a well-known formal verification benchmark called PutnamBench, which consists of 672 hard math problems from the William Lowell Putnam Exam, the oldest collegiate mathematics competition in North America. Backed by a world-class team – including ICPC champions, a Fields Medalist and an ACM Turing Award winner – we're building the future where all code is provably correct.

About the role

Join our team as an AI Engineer and help us push the boundaries of what's possible in logical reasoning! We're looking for a motivated individual to design and refine the data and ML pipelines for scaled distributed training and validation of ML models. You'll work closely with a talented team of AI experts, EBM specialists, formal verification engineers, and software developers to create groundbreaking solutions.

What you'll do
  • Research new reasoning algorithms and models
  • Develop model benchmarking processes and tools
  • Build effective and efficient ML data pipelines
  • Adjust frameworks and interfaces to accelerate machine learning development
  • Develop the infrastructure for data augmentation pipelines and synthetic data generation
  • Collaborate with other teams to understand their pain points and priorities to define milestones of the corresponding roadmaps
  • Derive practical solutions and integrate them with the results of other teams to provide the best overall resolution
Qualifications
  • You have an M.Sc. focusing on one or more of the following areas: Computer Science, Artificial Intelligence, Mathematics, or a closely related field
  • 3+ years of production experience in ML Infra, DataOps, distributed training
  • Expertise in programming languages and tools critical for high-performance computing in Python/C++ and machine learning including Deep Learning frameworks like PyTorch /TensorFlow/JAX
  • Ability to understand deep learning algorithms, e.g. in natural language processing, reasoning
  • Familiarity with Azure/AWS/GCP cloud products for MLOps and DataOps pipelines
  • Proficiency with Kubernetes clusters and distributed compute assets
  • Strong communication and teamwork skills
  • Readiness to explore and promote cutting edge technologies in ML Infrastructure domain and beyond

Bonus Points

  • Demonstrated publications in any of the major conferences
  • Multi-node and multi-GPU training
  • Mathematical Reasoning – discrete math and logic
  • Formal Verification - lean

logicalintelligence.com