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Data Quality Jobs in California (NOW HIRING)

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Data Quality information

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How much do data quality jobs pay per hour?

As of Aug 17, 2026, the average hourly pay for data quality in California is $40.89, according to ZipRecruiter salary data. Most workers in this role earn between $27.50 and $53.61 per hour, depending on experience, location, and employer.

What is a data quality?

A Data Quality job involves ensuring that data is accurate, consistent, and reliable for business use. Professionals in this role develop and enforce data quality standards, identify and resolve data discrepancies, and implement processes for data validation and cleansing. They often work with databases, data governance frameworks, and analytics teams to maintain high-quality data. This role is essential for organizations relying on data-driven decisions, as poor data quality can lead to incorrect insights and inefficiencies.

What are the typical challenges faced by someone working in a data quality role?

Professionals in Data Quality roles often encounter challenges such as identifying inconsistent data sources, addressing missing or inaccurate data, and maintaining data standards as systems and business requirements evolve. Working closely with IT, data analysts, and business stakeholders, Data Quality specialists must resolve data discrepancies while balancing the need for accuracy with project deadlines. These challenges require excellent analytical and troubleshooting skills, as well as the ability to communicate data issues clearly across teams. Overcoming these hurdles is key to ensuring data-driven decisions are based on trustworthy information.

What are the key skills and qualifications needed to thrive in a data quality position, and why are they important?

To thrive in a Data Quality role, you need expertise in data analysis, attention to detail, knowledge of data governance, and often a bachelor's degree in a related field such as computer science or information systems. Familiarity with tools like SQL, data profiling software, and data quality management platforms, as well as certifications like CDMP (Certified Data Management Professional), is highly valued. Strong problem-solving abilities, effective communication, and a collaborative mindset help professionals excel in this position. These skills are crucial for ensuring accurate, reliable data that supports business decision-making and overall organizational efficiency.

Is data quality a good career?

Data quality is a valuable career path involving ensuring the accuracy, consistency, and reliability of data within organizations. Professionals in this field often work with data management tools, perform audits, and may pursue certifications like Certified Data Management Professional (CDMP). It offers opportunities across industries such as finance, healthcare, and technology with steady demand for skilled data quality specialists.

What is the work of data quality?

Data quality involves ensuring that data is accurate, complete, consistent, and reliable for analysis and decision-making. Data quality professionals often use tools like data profiling and validation techniques to identify and correct errors, maintaining high standards for data integrity within organizations.

What are the most commonly searched types of Data Quality jobs in California?

The most popular types of Data Quality jobs in California are:

What are popular job titles related to Data Quality jobs in California?

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

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

The top searched job categories for Data Quality jobs in California are:

What cities in California are hiring for Data Quality jobs?

Cities in California with the most Data Quality job openings:

Data Quality jobs near you
Infographic showing various Data Quality job openings in California as of August 2026, with employment types broken down into 1% As Needed, 85% Full Time, 10% Part Time, 3% Contract, and 1% Nights. Highlights an 86% Physical, 2% Hybrid, and 12% Remote job distribution, with an average salary of $85,059 per year, or $40.9 per hour.

Lead Research Engineer, Data Quality

Clera

San Francisco, CA • On-site

$150K - $250K/yr

Full-time

Posted 13 days ago

Job description

About the Role

We're an early-stage AI/ML infrastructure company (11–50 people) building the platform that AI labs and businesses use to create, manage, and scale reinforcement learning (RL) environments and high-quality post-training datasets. We're looking for a Lead Research Engineer, Data Quality to own the strategy and systems that measure, improve, and scale training data for frontier agents.

In this role, you'll lead the data quality team, shape our internal research culture, and define what makes agent training data truly useful — not just superficially correct. You'll work on-site in San Francisco, CA. Visa sponsorship is available.

What You'll Do

  • Lead the data quality team in building systems that evaluate thousands of tasks across RL environments, synthetic data, benchmarks, and domain-specific workflows.

  • Define the company's data quality strategy — build QC systems, enforce standards, and design experiments to grade agent outputs.

  • Develop novel methods for validating synthetic data at scale, including failure-mode analysis, task mutation checks, and trajectory auditing.

  • Partner with research engineers, domain experts, and data vendors to diagnose quality issues and improve data generation workflows.

  • Translate qualitative research insights into production systems: internal tools, dashboards, validation pipelines, and feedback loops.

  • Help build internal research taste around what makes agent training data realistic, learnable, diverse, reliable, and useful.

  • Mentor research engineers to maintain a high bar for technical rigor, clarity, and execution speed.

What We're Looking For

Required (dealbreakers):

  • 5+ years of experience in a research or data quality engineering role, specifically building systems for AI/ML data evaluation.

  • Demonstrated experience leading technical projects or teams in data quality or AI/ML evaluation.

  • Advanced proficiency in Python, Docker, and Linux environments.

Also required:

  • Ability to reason deeply about characteristics of high-quality training data (realistic, learnable, diverse, reliable) for AI agents.

  • Experience translating research insights into production systems and data pipelines (e.g., validation pipelines, feedback loops).

  • Experience developing and implementing methods for validating synthetic data at scale.

  • Experience collaborating with research engineers, domain experts, and data vendors to diagnose quality issues.

  • Track record mentoring engineers on technical rigor and execution speed.

  • Comfort navigating complex systems involving domain experts, vendors, generated data, model outputs, graders, and infrastructure.

Nice to have:

  • Experience leading teams on ambiguous technical projects from problem definition through implementation and iteration.

  • Experience working with subject-matter experts to convert domain judgment into scalable review or generation systems.

  • Experience designing metrics, experiments, and QA/QC processes from scratch.

  • Prior early-stage startup experience and comfort operating independently in fast-paced environments.

  • Strong written communication skills — able to explain methodology clearly to researchers, engineers, and external audiences.

Compensation & Benefits

  • Salary: $150,000 – $250,000 USD annually, depending on experience.

  • Early-stage equity participation.

  • Visa sponsorship available.

Location

This is an on-site role based in San Francisco, CA. Candidates must be willing to work in-office. Visa sponsorship is available for qualified candidates.