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Dbt Analytics Jobs in California (NOW HIRING)

Data Analytics Engineer

Calabasas, CA · On-site

$90K - $100K/yr

Governed semantic models in dbt for our top business KPIs: bookings, occupancy, revenue ... Cortex Analyst semantic YAML for natural-language data exploration by our internal users. * Data ...

We are midway through a strategic migration to dbt as our transformation standard. Beyond that, our ... As a Senior Analytics Engineer, you will help drive that journey end-to-end. This role is grounded ...

Senior BI / Analytics Engineer

Los Angeles, CA · On-site

$112K - $154K/yr

The ideal candidate brings deep expertise in dbt, Snowflake, Power BI, and modern ingestion platforms, with a strong foundation in data modeling, analytics engineering, and business intelligence best ...

Senior Analytics Engineer, GTM

Santa Clara, CA · On-site

$122K - $168K/yr

We are midway through a strategic migration to dbt as our transformation standard. Beyond that, our ... As a Senior Analytics Engineer, you will help drive that journey end-to-end. This role is grounded ...

Maintain deep fluency in the data models, metrics, and pipelines (SQL, dbt, Snowflake) that power GTM reporting. Partner closely with Analytics Engineering and Data Engineering teams to ensure ...

New

Build scalable analytical models and transformations using SQL, dbt, Python, Spark, or similar technologies. * Establish strong engineering practices for architecture reviews, testing, documentation ...

Build scalable analytical models and transformations using SQL, dbt, Python, Spark, or similar technologies. * Establish strong engineering practices for architecture reviews, testing, documentation ...

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Dbt Analytics information

See California salary details

$37K

$63.1K

$90.8K

How much do dbt analytics jobs pay per year?

As of Aug 16, 2026, the average yearly pay for dbt analytics in California is $63,067.00, according to ZipRecruiter salary data. Most workers in this role earn between $52,300.00 and $70,100.00 per year, depending on experience, location, and employer.

What is a dbt analytics?

A dbt Analytics job typically refers to a role focused on using dbt (data build tool) to transform, test, and document data within a data warehouse. Professionals in this field design and manage data models, write SQL-based transformations, and ensure data quality for analytics purposes. They collaborate with data engineers and analysts to build efficient, maintainable workflows that support business intelligence and data-driven decision-making. dbt Analytics jobs require strong SQL skills, familiarity with modern data stack tools, and an understanding of data modeling best practices.

What are the key skills and qualifications needed to thrive as a dbt analytics professional?

To thrive as a DBT Analytics professional, you need strong SQL skills, a solid understanding of data modeling, and experience with analytics engineering, typically backed by a degree in a quantitative field. Familiarity with the DBT (Data Build Tool) platform, cloud data warehouses like Snowflake or BigQuery, and version control systems such as Git is essential. Attention to detail, problem-solving abilities, and effective communication help you collaborate with stakeholders and ensure data reliability. These skills and tools are crucial for transforming raw data into actionable insights and maintaining robust, scalable analytics infrastructure.

How does a dbt analytics professional typically collaborate with data engineers and analysts within a team?

Dbt Analytics professionals play a key role in bridging the work of data engineers and data analysts. They transform raw data into clean, well-documented, and analysis-ready datasets using dbt (data build tool), ensuring consistency and reliability. Collaboration often involves working closely with data engineers to understand data sources and pipelines, while also partnering with analysts to tailor data models to business needs. Effective communication and regular feedback loops are crucial, as dbt professionals often serve as the link between technical data infrastructure and business-facing analysis.

What is the difference between Dbt Analytics vs Data Analyst?

AspectDbt AnalyticsData Analyst
Required CredentialsSQL, data modeling, analytics certificationsStatistics, Excel, SQL, sometimes certifications
Work EnvironmentData teams, analytics platforms, cloud environmentsBusiness units, reporting tools, spreadsheets
Industry UsageData transformation, modeling, analytics pipelinesData interpretation, reporting, insights

While Dbt Analytics focuses on transforming and modeling data within analytics workflows, Data Analysts primarily interpret data and generate reports. Both roles require SQL skills and work closely with data teams, but Dbt Analytics emphasizes data transformation using tools like dbt, whereas Data Analysts focus on analyzing and communicating insights.

Is dbt analytics in demand?

Dbt analytics roles are increasingly in demand as companies focus on data transformation and analytics workflows. Skills in SQL, data modeling, and tools like dbt are valuable for data analysts and engineers working in modern data environments.

What are the most commonly searched types of Dbt Analytics jobs in California?

The most popular types of Dbt Analytics jobs in California are:

What cities in California are hiring for Dbt Analytics jobs?

Cities in California with the most Dbt Analytics job openings:

Infographic showing various Dbt Analytics job openings in California as of August 2026, with employment types broken down into 1% Internship, 89% Full Time, 5% Part Time, 2% Temporary, and 3% Contract. Highlights an 78% Physical, 7% Hybrid, and 15% Remote job distribution, with an average salary of $63,067 per year, or $30.3 per hour.

Senior Analytics Engineer, GTM

Everpure

Santa Clara, CA • On-site

Full-time

Re-posted 7 days ago


Job description

Job Summary:
Everpure is fundamentally reshaping the data storage industry and is seeking a Senior Analytics Engineer to drive modernization initiatives. In this role, you will lead efforts in building scalable analytics solutions, collaborating with various teams to enhance data engineering practices and improve operational efficiency.
Responsibilities:
• Lead and contribute hands-on to the team’s dbt and Snowflake modernization initiatives
• Build scalable semantic and metrics layers supporting GTM domains including pipeline, forecasting, quota, attainment, and seller performance
• Design and maintain robust ELT/ETL pipelines, orchestration workflows, and reusable business-facing datasets
• Build CI/CD, testing, observability, lineage, and data quality frameworks for analytics engineering workflows
• Ingest and transform data from APIs and GTM systems into curated canonical datasets with strong quality standards
• Drive architecture decisions and establish modern analytics engineering best practices across the team
• Partner closely with BIAs, RevOps, and Systems teams to translate business problems into scalable data products
• Help automate manual reporting and operational workflows through configurable and scalable frameworks
• Apply AI capabilities to workflow automation, operational efficiency, and insight generation, while helping lay the foundation for future conversational analytics initiatives
Qualifications:
Required:
• 7+ years of experience in analytics engineering, data engineering, or business intelligence
• Strong expertise in SQL, Python, Snowflake, and modern ELT development practices
• Hands-on experience with dbt, orchestration workflows, and scalable data modeling
• Experience implementing CI/CD, automated testing, observability, and data quality frameworks
• Strong understanding of semantic modeling, metrics layers, governed metrics, and reusable analytics datasets
• Experience using AI to build workflows, automate processes, improve operational efficiency, or support business intelligence use cases
• Ability to influence technical direction while remaining hands-on
• Strong communication and collaboration skills across technical and business teams
• Pragmatic interest in applying AI capabilities to real business workflows over time
• Passion for modernization, reporting trust, and scalable analytics engineering
Preferred:
• GTM, Sales, Revenue Operations, or forecasting analytics domains
• Salesforce, Clari, Anaplan, Workday, or related enterprise systems
• Analytics engineering modernization initiatives
• Self-service analytics ecosystems and semantic layers
• Conversational analytics, AI copilots, or workflow automation concepts
• Experience migrating legacy SQL or ETL frameworks into modern dbt-based transformation workflows
• Tableau or modern BI visualization platforms
Company:
We are Everpure. We don’t just store data—we bring it to life. Founded in , the company is headquartered in , , with a team of 5001-10000 employees. The company is currently Late Stage.