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Data Governance Engineer Jobs in California (NOW HIRING)

Rippling

Data Governance Manager - Hybrid

Santa Monica, CA · On-site

$140 - $180/hr

You'll work across Data Platform/Engineering, Legal & Privacy, Security, Dealer & OEM Product ... Access Governance * Maintain a current access inventory: which roles, teams, and systems can access ...

Qualcomm

Data Engineer, Staff

San Diego, CA · On-site

$132 - $198/hr

Implement security, compliance, and data governance controls across shared data assets. * Drive ... Mentor junior engineers and provide technical guidance, including best practices for automating ...

Qualcomm

Data Engineer, Staff

San Diego, CA · On-site

$121K - $146K/yr

Implement security, compliance, and data governance controls across shared data assets * Drive ... Mentor junior engineers and provide technical guidance, including best practices for automating ...

Apex Informatics

AWS Data Engineer

San Francisco, CA · On-site

$134K - $162K/yr

AWS Data Engineer Location: San Francisco and jersey city ( 3 days onsite 2 days remote) look for ... Comply with data governance principles and best practices to ensure data security, privacy, and ...

Qualcomm

Data Engineer, Senior Staff

San Diego, CA · On-site

$121K - $146K/yr

Implement security, compliance, and data governance controls across shared data assets * Drive ... Mentor junior engineers and provide technical guidance, including best practices for automating ...

Jobtailor

Staff, Data Engineer

Sunnyvale, CA · On-site

$140 - $180/hr

Implement data governance practices for regulatory and ethical compliance * Automate workflows and ... Collaborate with product, data science, and other data engineering teams, owning model quality ...

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All JobsData Governance Engineer JobsData Governance Engineer Jobs in California

Data Governance Engineer information

See California salary details

$43.9K

$128K

$175.2K

How much do data governance engineer jobs pay per year?

As of Aug 22, 2026, the average yearly pay for data governance engineer in California is $128,018.00, according to ZipRecruiter salary data. Most workers in this role earn between $113,000.00 and $135,700.00 per year, depending on experience, location, and employer.

What is a data governance engineer?

Data Governance Engineers are professionals responsible for designing, implementing, and maintaining systems and processes that ensure data is accurate, consistent, secure, and compliant with relevant regulations. They work closely with data stewards, IT teams, and business units to create data policies, manage metadata, and oversee data quality initiatives. Their role is crucial in helping organizations maximize the value of their data assets while minimizing risks related to data privacy and misuse.

What are the key skills and qualifications needed to thrive as a data governance engineer?

To thrive as a Data Governance Engineer, you need a solid understanding of data management principles, metadata standards, data quality frameworks, and typically a degree in computer science, information systems, or a related field. Familiarity with data governance tools like Collibra, Informatica, or Alation, as well as proficiency in SQL, data modeling, and regulatory compliance frameworks such as GDPR, is essential. Strong analytical thinking, attention to detail, and effective communication skills are crucial for collaborating with stakeholders and ensuring adherence to data policies. These skills and qualities are vital to maintain data integrity, compliance, and support informed business decision-making.

How does a data governance engineer typically collaborate with data stewards and business units to ensure data quality?

A Data Governance Engineer works closely with data stewards and various business units to define, implement, and monitor data quality standards. This involves facilitating cross-functional meetings, translating business requirements into technical data policies, and creating automated processes to enforce those standards. Regular collaboration ensures that data assets are accurate, secure, and compliant with regulations, while also supporting business goals. By acting as a bridge between technical teams and business stakeholders, Data Governance Engineers help foster a culture of data accountability and transparency.

Is data governance a good career?

Data Governance Engineer is a growing role focused on managing data quality, security, and compliance within organizations. It requires knowledge of data management tools, policies, and regulations, making it a stable and in-demand career path with opportunities for advancement. The field offers a blend of technical and strategic work, suitable for those interested in data integrity and organizational compliance.

What does a data governance engineer do?

A data governance engineer is responsible for establishing and maintaining data management policies, standards, and procedures to ensure data quality, security, and compliance. They often work with data catalogs, metadata management tools, and collaborate with data stewards and IT teams to implement data governance frameworks. Strong knowledge of data privacy regulations and data modeling is essential for this role.

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

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

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

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

What cities in California are hiring for Data Governance Engineer jobs?

Cities in California with the most Data Governance Engineer job openings:

Data Governance Engineer jobs near you
Infographic showing various Data Governance Engineer job openings in California as of August 2026, with employment types broken down into 1% As Needed, 88% Full Time, 9% Part Time, and 2% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution, with an average salary of $128,018 per year, or $61.5 per hour.

Senior Manager - Data & AI Governance

Mercury

San Francisco, CA • On-site, Remote

Full-time

Posted 8 days ago

Job description

Mercury is seeking a Data & AI Governance leader to build and spearhead an enterprise-wide governance program for data and artificial intelligence. Reporting to the Chief Risk Officer, this leader will establish practical standards for how data and AI are owned, developed, used, protected, and monitored across the organization.
The ideal candidate combines strong governance and risk-management experience with sufficient technical fluency to work effectively with Data, Engineering, Product, Information Security, Legal, Compliance, and business teams. This person should be comfortable building in a fast-moving environment and designing governance that supports responsible innovation without creating unnecessary complexity.
This role will work closely with the Model Risk Management and Information Security teams while maintaining a distinct mandate: Data & AI Governance will establish enterprise governance and responsible-use standards, while Model Risk Management will retain responsibility for model inventory, tiering, validation, and model-risk oversight.
Key Responsibilities:
  • Develop and implement Mercury's enterprise Data and AI Governance frameworks, policies, standards, and operating model.
  • Establish clear accountability for data ownership, stewardship, quality, lineage, classification, access, retention, and appropriate use.
  • Create a risk-based governance process for AI use cases across their lifecycle, including intake, assessment, approval, implementation, monitoring, and retirement.
  • Develop responsible-AI principles and standards addressing transparency, explainability, fairness, privacy, security, human oversight, reliability, and regulatory compliance.
  • Maintain an enterprise inventory of material data assets, AI use cases, and related governance decisions in coordination with relevant stakeholders.
  • Define risk-based classifications and governance requirements based on the sensitivity, complexity, materiality, and customer or regulatory impact of each use case.
  • Establish governance for internally developed, vendor-provided, and embedded AI capabilities, including generative AI.
  • Partner with various Product, Engineering, Data, and business teams to embed governance requirements into development and change-management processes.
  • Coordinate with Model Risk Management to determine when an AI use case meets the definition of a model and is subject to model-risk requirements.
  • Partner with Information Security and Technology Risk on data protection, cybersecurity, access, architecture, resilience, and technology-control considerations.
  • Partner with Legal and Compliance to identify and implement applicable regulatory, contractual, consumer-protection, and privacy requirements.
  • Develop processes for identifying, documenting, escalating, and remediating data- and AI-related risks and issues.
  • Establish metrics/reporting to provide management and Board committees with visibility into data quality, governance maturity, AI adoption, exceptions, incidents, and emerging risks.
  • Monitor regulatory developments, industry practices, and emerging risks related to data and AI, and translate them into proportionate governance expectations.
  • Support relevant Data and AI governance forums/committees and facilitate timely, well-documented decisions.
  • Eventually, build and lead a high-performing Data & AI Governance team as the program matures.
  • Promote a culture in which data is treated as an enterprise asset and AI is used responsibly, transparently, and in alignment with Mercury's risk appetite.
Qualifications:
  • 10+ years of relevant experience in data governance, AI governance, technology risk, information governance, model risk, privacy, compliance, or a related discipline.
  • Demonstrated experience building or materially enhancing a data governance, AI governance, or responsible-AI program.
  • Strong understanding of data ownership, stewardship, quality, lineage, metadata, classification, access, retention, and lifecycle management.
  • Working knowledge of AI and machine-learning concepts, including generative AI, large language models, training and inference data, explainability, bias, performance monitoring, and human oversight.
  • Experience developing practical, risk-based policies and governance processes that can operate effectively in a fast-moving technology environment.
  • Ability to distinguish among data governance, AI governance, model risk, information security, privacy, and compliance responsibilities while coordinating effectively across those functions.
  • Strong judgment and the ability to balance innovation, customer outcomes, regulatory expectations, and risk management.
  • Demonstrated ability to influence senior executives, technical teams, and business leaders without relying solely on formal authority.
  • Excellent written and verbal communication skills, including the ability to explain complex technical and risk concepts to executive and Board audiences.
  • Experience leading teams and managing cross-functional programs with multiple stakeholders.
  • Strong 1LOD/2LOD judgment with an understanding of how enterprise Risk should govern, challenge, and partner with Engineering without taking ownership of 1LOD risks.
  • Pragmatic judgment: Translates principles into workable processes and focuses on material risks over theoretical ones.
  • Technical curiosity: Understands technical complexity while staying focused on business and customer outcomes.
  • Decisive and collaborative: Makes sound decisions amid ambiguity, moves quickly, and challenges constructively across teams
Preferred Qualifications:
  • Experience within a fintech, financial institution, technology company, or other highly regulated environment.
  • Familiarity with banking regulatory expectations for data management, model risk, third-party risk, privacy, consumer protection, and information security.
  • Experience with recognized data- and AI-governance frameworks and standards, such as DAMA-DMBOK, NIST AI RMF, ISO/IEC 42001, or comparable frameworks.
  • Experience governing third-party data, vendor AI solutions, and embedded AI capabilities.
  • Technical or analytical experience in data architecture, data engineering, machine learning, analytics, or software development.
  • Experience operating in a company-building or bank-building environment

*Mercury is a fintech company, not an FDIC-insured bank. Banking services provided through Choice Financial Group and Column N.A., Members FDIC.
Compensation:
The total rewards package at Mercury includes base salary, equity (stock options/RSUs), and benefits.
Our salary and equity ranges are highly competitive within the SaaS and fintech industry and are updated regularly using the most reliable compensation survey data for our industry. New hire offers are made based on a candidate's experience, expertise, geographic location, and internal pay equity relative to peers.
Our target new hire base salary ranges for this role are the following:
  • US employees in New York City, Los Angeles, Seattle, or the San Francisco Bay Area: $225,800 - $282,300 USD
  • US employees outside of New York City, Los Angeles, Seattle, or the San Francisco Bay Area: $203,300 - $254,100 USD

Mercury values diversity & belonging and is proud to be an Equal Employment Opportunity employer. All individuals seeking employment at Mercury are considered without regard to race, color, religion, national origin, age, sex, marital status, ancestry, physical or mental disability, veteran status, gender identity, sexual orientation, or any other legally protected characteristic. We are committed to providing reasonable accommodations throughout the recruitment process for applicants with disabilities or special needs. If you need assistance, or an accommodation, please let your recruiter know once you are contacted about a role.
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