ZipRecruiter

Log In

1

Data Analytics Engineer Jobs in Santa Rosa, CA (NOW HIRING)

Block

Staff Data Scientist, Pricing

Bodega Bay, CA · On-site

Experience with scripting and data analysis programming languages, such as Python or R, including using them to evaluate AI system behavior * Gone deep with cohort and funnel analyses, with a solid ...

PROPRIUS

Machine Learning / Data Scientist

Bodega Bay, CA · On-site

$110K - $140K/yr

As a ML Engineer, you will: * Work with stakeholders throughout the organization to identify opportunities for leveraging data to drive business solutions * Mine and analyze data from databases to ...

NE-NERSC

HPC Scientific Support Engineer

Bodega Bay, CA · On-site

$158K - $267K/yr

Experience with AI and data analytics software stacks. * Strong background in mathematics and statistics. * Web programming (REST, JavaScript) and database skills (SQL). Required Application ...

Apple

Display Module Engineer

Bodega Bay, CA

$129K - $225K/yr

... data analysis to ensure our displays meet Apple's world-class quality standards Work closely with system engineering teams to validate display modules at the system level, investigate and resolve ...

previous pagenext page

Showing results 21-40

All JobsData Analytics Engineer JobsData Analytics Engineer Jobs in Santa Rosa, CA

Data Analytics Engineer information

See Santa Rosa, CA salary details

$48.7K

$141.8K

$194.1K

How much do data analytics engineer jobs pay per year?

As of Aug 20, 2026, the average yearly pay for data analytics engineer in Santa Rosa, CA is $141,823.00, according to ZipRecruiter salary data. Most workers in this role earn between $125,200.00 and $150,300.00 per year, depending on experience, location, and employer.

How do data analytics engineers typically collaborate with data scientists and business stakeholders on projects?

Data Analytics Engineers play a crucial role in bridging the gap between raw data and actionable insights by building, optimizing, and maintaining data pipelines. They often work closely with data scientists to ensure data is clean, accessible, and structured for advanced analytics or machine learning models. Additionally, they collaborate with business stakeholders to understand reporting requirements and ensure that data solutions align with organizational objectives. Regular communication and cross-functional teamwork are essential aspects of this role, as engineers must translate business needs into technical specifications and deliver reliable data products.

What are the key skills and qualifications needed to thrive as a data analytics engineer, and why are they important?

To thrive as a Data Analytics Engineer, you need strong proficiency in data modeling, SQL, and statistical analysis, typically supported by a degree in computer science, statistics, or a related field. Familiarity with tools such as Python, R, Apache Spark, Tableau, and cloud data platforms like AWS or Google BigQuery is essential, along with relevant certifications. Excellent problem-solving, communication, and collaboration skills help you translate data insights into actionable business solutions. These skills and qualities are crucial for designing robust data pipelines and enabling data-driven decision-making across organizations.

What is the difference between Data Analytics Engineer vs Data Scientist?

AspectData Analytics EngineerData Scientist
CredentialsBachelor's or master's in CS, Data Science, or related fields; certifications like Google Data AnalyticsBachelor's or master's in CS, Statistics, or related fields; certifications like Certified Data Scientist
Work EnvironmentFocus on building data pipelines, dashboards, and analytics toolsFocus on statistical modeling, machine learning, and data exploration
Employer & Industry UsageUsed across tech, finance, healthcare for data infrastructure and analyticsCommon in research, product development, and advanced analytics teams

While both roles work with data, Data Analytics Engineers primarily develop data infrastructure and tools for analysis, whereas Data Scientists focus on statistical modeling and machine learning to generate insights. They often collaborate but have distinct technical focuses.

What does a data analytics engineer do?

A data analytics engineer designs, builds, and maintains data pipelines and infrastructure to collect, process, and analyze large datasets. They use tools like SQL, Python, and cloud platforms to enable data-driven decision-making and often collaborate with data scientists and business teams to develop insights and reports.

What are the most commonly searched types of Data Analytics Engineer jobs in Santa Rosa, CA?

The most popular types of Data Analytics Engineer jobs in Santa Rosa, CA are:

What are popular job titles related to Data Analytics Engineer jobs in Santa Rosa, CA?

For Data Analytics Engineer jobs in Santa Rosa, CA, the most frequently searched job titles are:

What cities near Santa Rosa, CA are hiring for Data Analytics Engineer jobs?

Cities near Santa Rosa, CA with the most Data Analytics Engineer job openings:

Data Analytics Engineer jobs near you
Infographic showing various Data Analytics Engineer job openings in Santa Rosa, CA as of August 2026, with employment types broken down into 77% Full Time, and 23% Contract. Highlights an 100% In-person job distribution, with an average salary of $141,823 per year, or $68.2 per hour.

IT Director, Data Services and AI Enablement

HeartFlow

Rohnert Park, CA • On-site

Other

This job post has expired today. Applications are no longer accepted.

HeartFlow rating

7.8

Company rating: 7.8 out of 10

Based on 10 frontline employees who took The Breakroom Quiz

137th of 245 rated software companies

Job description

Heartflow is a medical technology company advancing the diagnosis and management of coronary artery disease, the #1 cause of death worldwide, using cutting-edge technology. The flagship product-an AI-driven, non-invasive cardiac test supported by the ACC/AHA Chest Pain Guidelines called the Heartflow FFRCT Analysis-provides a color-coded, 3D model of a patient's coronary arteries indicating the impact blockages have on blood flow to the heart. Heartflow is the first AI-driven non-invasive integrated heart care solution across the CCTA pathway that helps clinicians identify stenoses in the coronary arteries (RoadMap Analysis), assess coronary blood flow (FFRCT Analysis), and characterize and quantify coronary atherosclerosis (Plaque Analysis). Our pipeline of products is growing and so is our team; join us in helping to revolutionize precision heartcare.
Heartflow is a publicly traded company (HTFL) that has received international recognition for exceptional strides in healthcare innovation, is supported by medical societies around the world, cleared for use in the US, UK, Europe, Japan and Canada, and has been used for more than 500,000 patients worldwide.
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
Heartflow is an Equal Opportunity Employer. We are committed to a work environment that supports, inspires, and respects all individuals and do not discriminate against any employee or applicant because of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, veteran status, or any other status protected under federal, state, or local law. This policy applies to every aspect of employment at Heartflow, including recruitment, hiring, training, relocation, promotion, and termination.
Positions posted for Heartflow are not intended for or open to third party recruiters / agencies. Submission of any unsolicited resumes for these positions will be considered to be free referrals.
Heartflow has become aware of a fraud where unknown entities are posing as Heartflow recruiters in an attempt to obtain personal information from individuals as part of our application or job offer process. Before providing any personal information to outside parties, please verify the following: A) all legitimate Heartflow recruiter email addresses end with "@heartflow.com" and B) the position described is found on our careers site at

What HeartFlow employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom

Apply