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Director Data Management Jobs in Santa Rosa, CA (NOW HIRING)

... data, CRM and billing. • Evaluate the existing data foundation, identify what is missing and make thoughtful build-versus-buy decisions involving governance, semantic layers, modern analytics ...

... data, CRM and billing. • Evaluate the existing data foundation, identify what is missing and make thoughtful build-versus-buy decisions involving governance, semantic layers, modern analytics ...

This role reports to the Senior Director, Data Products and is based in our San Francisco office ... management or equivalent relevant experience with meaningful time spent on data products, data ...

Director Clinical Affairs

Sonoma, CA · On-site

$168K - $221K/yr

Director of Clinical Affairs We're seeking a Director of Clinical Affairs to own end-to-end ... Manage clinical data operations -- eCRF design, data review, query resolution, vendor coordination

Director Clinical Affairs

Santa Rosa, CA · On-site

$164K - $216K/yr

Director of Clinical Affairs We're seeking a Director of Clinical Affairs to own end-to-end ... Manage clinical data operations -- eCRF design, data review, query resolution, vendor coordination

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Director Data Management information

See Santa Rosa, CA salary details

$59K

$169.3K

$266.8K

How much do director data management jobs pay per year?

As of Sep 1, 2026, the average yearly pay for director data management in Santa Rosa, CA is $169,328.00, according to ZipRecruiter salary data. Most workers in this role earn between $120,300.00 and $207,200.00 per year, depending on experience, location, and employer.

What is the difference between Director Data Management vs Data Analyst?

AspectDirector Data ManagementData Analyst
Required CredentialsBachelor's or Master's in Data Science, IT, or related field; often with leadership experienceBachelor's in Statistics, Data Science, or related field; often entry to mid-level experience
Work EnvironmentStrategic leadership, overseeing data teams, managing data governanceData collection, analysis, reporting, supporting decision-making
Employer & Industry UsageUsed in large corporations, tech, finance, healthcareCommon across industries for data-driven roles

The main difference is that a Director Data Management focuses on strategic oversight, data governance, and leading data teams, while a Data Analyst primarily handles data analysis, reporting, and supporting business decisions. The Director role involves higher-level management and planning, whereas Data Analysts execute specific data tasks.

More about Director Data Management jobs

What job categories do people searching Director Data Management jobs in Santa Rosa, CA look for?

The top searched job categories for Director Data Management jobs in Santa Rosa, CA are:

What cities near Santa Rosa, CA are hiring for Director Data Management jobs?

Cities near Santa Rosa, CA with the most Director Data Management job openings:

Infographic showing various Director Data Management job openings in Santa Rosa, CA as of August 2026, with employment types broken down into 1% As Needed, 80% Full Time, 15% Part Time, and 4% Contract. Highlights an 90% Physical, 2% Hybrid, and 8% Remote job distribution, with an average salary of $169,328 per year, or $81.4 per hour.

IT Director, Data Services and AI Enablement

Heartflow

Rohnert Park, CA • On-site

Full-time

Posted 26 days ago


HeartFlow rating

7.8

Company rating: 7.8 out of 10

Based on 10 frontline employees who took The Breakroom Quiz

138th of 247 rated software companies


Job description

The IT Director, Data Services and AI Enablement provides strategic leadership and operational oversight for Heartflow's data engineering, systems integrations and automation, and AI enablement functions. This role leads a small team responsible for data infrastructure, enterprise integrations, automated workflows, and AI-enabled solutions that support organizational effectiveness. 

This role drives the development and optimization of the enterprise data platform, delivering scalable, governed, high-quality data solutions that accelerate time-to-insight, improve reliability, and enable AI/ML and analytics through efficient, self-service access to analytics-ready data.

Data Infrastructure & Engineering

  • Lead the design, development, and management of enterprise data infrastructure platform owning the end-to-end data lifecycle, including ingestion (batch, streaming, APIs), transformation (ETL/ELT), modeling, storage, integration, and delivery of data products.
  • Oversee data pipelines, data modeling, and reporting solutions that support organizational decision-making while embedding governance, data quality, monitoring, and observability into workflows to reduce defects, latency, and operational inefficiencies.
  • Ensure data accuracy, consistency, and accessibility across systems and stakeholders.
  • Design and operationalize an enterprise semantic layer (e.g., Cube Cloud) to provide secure, context-rich, and standardized data access for AI applications and advanced analytics.

Analytics, AI Enablement, & Strategy

  • Drive the company's 'AI-readiness' by ensuring underlying data architectures are clean, structured, and highly available for advanced machine learning and generative AI workloads.
  • Enable self-service analytics and data discoverability through tools like Tableau, semantic layers, and data catalogs while maintaining governance and data integrity.
  • Lead the evaluation and implementation of AI-enabled tools and solutions that enhance decision-making and efficiency.
  • Partner with business units to identify, evaluate, and prioritize high-value AI use cases.
  • Partner with executive leadership to align data investments with corporate and digital transformation strategies.

Integration & Automation

  • Direct the design and implementation of integrations across enterprise applications.
  • Ensure integration reliability, scalability, and alignment with enterprise architecture.
  • Lead the development of automated workflows that reduce manual processes and improve operational efficiency.

Governance & Continuous Improvements

  • Support governance for data management, system integrations, and responsible use of data and AI.
  • Establish and track key performance indicators related to data quality, adoption, and automation impact.
  • Identify and implement improvements that enhance data reliability, efficiency, and user experience.
  • Partner with stakeholders to translate business needs into data and reporting solutions.
  • Partner with vendors and evaluate technologies aligned to enterprise data strategy and architecture.
  • Drive FinOps initiatives and cost management strategies to optimize cloud infrastructure spend while maintaining high performance and scalability. 

Educational Requirements & Work Experience

  • Education: Bachelor's degree in Computer Science, Information Systems, Data Science, Engineering, or a related technical field. (A Master's degree in a related field or Business Administration is highly preferred).
  • Certifications (Preferred): Relevant cloud or data architecture certifications (e.g., AWS Certified Data Analytics, AWS Certified Solutions Architect, or equivalent governance certifications).

Required Experience

  • Domain Expertise: 8+ years of progressive experience in data engineering, enterprise data architecture, or systems integration.
  • Strategic Leadership: 4+ years of direct leadership experience, with a proven track record of translating complex enterprise business requirements into scalable data and analytics strategies.
  • Modern Data Stack & Migrations: Demonstrated, hands-on leadership experience directing large-scale data architecture migrations. Must have deep familiarity with AWS infrastructure, cloud data warehousing (e.g., Redshift), and orchestration tools (e.g., Dagster).
  • BI & Analytics Transformation: Proven experience managing enterprise business intelligence platforms and leading large BI migrations (e.g., transitioning from Domo to PowerBI).
  • Enterprise Integration: Strong background in designing and managing complex integrations with core enterprise applications (e.g., Salesforce, NetSuite, ADP, Master Data Management).

Technical & AI Proficiencies

  • AI Readiness & Semantic Layers: Understanding of modern semantic layers (e.g., Cube Cloud) and how to architect data governance to enable AI, machine learning, and advanced self-service analytics.
  • Data Governance: Strong framework knowledge for establishing data quality, observability, and compliance across automated workflows.
  • Industry Context (Preferred): Previous experience in MedTech, Healthcare, or Life Sciences, with an understanding of handling regulated or sensitive data ecosystems.

A reasonable estimate of the base salary compensation range is $220,000 to $270,000 per year, bonus, and equity. #LI-IB1 #LI-Hybrid


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