ZipRecruiter

Log In

1

Dbt Data Engineer Jobs (NOW HIRING)

Blue Learning

Data Engineer

Lewisville, TX · On-site

$75K - $93K/yr

Write clean, optimized SQL and utilize DBT to transform raw data into production-ready models ... Experience: 1-3 years in data engineering, analytics engineering, or a data-heavy technical role.

Diverse Lynx

Senior ETL Data Engineer

Houston, TX · On-site

$101K - $137K/yr

AWS Glue & Lambda (8+ years overall ETL experience) DBT (Data Modelling) Snowflake (Data Warehouse ... engineering initiatives, ensuring high performance, data quality, and reliability Develop and ...

next page

Showing results 1-20

All JobsDbt Data Engineer Jobs

Dbt Data Engineer information

See salary details

$44.5K

$129.7K

$177.5K

How much do dbt data engineer jobs pay per year?

As of Jul 5, 2026, the average yearly pay for dbt data engineer in the United States is $129,716.00, according to ZipRecruiter salary data. Most workers in this role earn between $114,500.00 and $137,500.00 per year, depending on experience, location, and employer.

Is dbt in demand?

Yes, dbt Data Engineers are in demand as organizations increasingly adopt modern data transformation tools to improve data workflows. Skills in SQL, data modeling, and familiarity with cloud platforms enhance job prospects in this field.

What engineer makes $500,000 a year?

Highly experienced data engineers, including those working with advanced big data tools like Apache Spark and cloud platforms, can earn salaries approaching or exceeding $500,000 annually, especially in senior or specialized roles at large tech companies. Achieving this level typically requires extensive expertise, certifications, and a strong track record in data architecture and engineering.

What are the key skills and qualifications needed to thrive as a Dbt Data Engineer, and why are they important?

To thrive as a Dbt Data Engineer, you need strong SQL skills, experience in data modeling, and a solid understanding of ELT/ETL pipelines, often supported by a degree in computer science or a related field. Familiarity with dbt (data build tool), version control systems like Git, and cloud data platforms such as Snowflake or BigQuery is typically required. Attention to detail, problem-solving abilities, and effective collaboration are essential soft skills for this role. These skills ensure robust, scalable, and maintainable data transformations that drive reliable analytics and business insights.

Are data engineers still in demand?

Data engineers are currently in high demand due to the increasing need for managing large-scale data pipelines, cloud platforms, and data integration tools. Skills in SQL, Python, and cloud services like AWS or Azure enhance job prospects in this field.

How does a Dbt Data Engineer typically collaborate with data analysts and other stakeholders?

As a Dbt Data Engineer, you'll work closely with data analysts, business intelligence teams, and sometimes product managers to translate business requirements into reliable, well-structured data models. Collaboration often involves reviewing transformation logic, ensuring data quality, and providing documentation or training on Dbt models. You may also participate in regular stand-ups or data modeling sessions to align on priorities and address data challenges collaboratively. Effective communication skills are key, as you'll bridge the gap between raw data and actionable insights.

What are Dbt Data Engineers?

Dbt Data Engineers are professionals who specialize in using dbt (data build tool) to transform, test, and document data within modern data warehouses. They build and maintain data pipelines by writing SQL-based transformation scripts and ensuring data quality through automated testing. Dbt Data Engineers collaborate closely with analytics teams to create reliable, well-documented datasets that support business intelligence and analytics initiatives.

Do data engineers use dbt?

Data engineers often use dbt (data build tool) to transform and model data within data warehouses. It is a popular tool for managing data pipelines, enabling version control, testing, and documentation, which are key responsibilities of data engineers.

What is the difference between Dbt Data Engineer vs Data Analyst?

AspectDbt Data EngineerData Analyst
Primary FocusBuilding and maintaining data transformation pipelines using dbtAnalyzing data to generate reports and insights
Skills & ToolsSQL, dbt, ETL pipelines, cloud platformsSQL, Excel, BI tools, data visualization
Work EnvironmentData engineering teams, cloud data platformsBusiness units, reporting teams
CertificationsSQL, cloud certifications, dbt trainingData analysis, visualization certifications

While both roles work with data and SQL, Dbt Data Engineers focus on developing scalable data transformation pipelines using dbt, whereas Data Analysts primarily analyze data to produce reports and insights. The roles complement each other within data teams but differ in technical scope and responsibilities.

More about Dbt Data Engineer jobs
What cities are hiring for Dbt Data Engineer jobs? Cities with the most Dbt Data Engineer job openings:
What states have the most Dbt Data Engineer jobs? States with the most job openings for Dbt Data Engineer jobs include:
What job categories do people searching Dbt Data Engineer jobs look for? The top searched job categories for Dbt Data Engineer jobs are:
Dbt Data Engineer jobs near you
Infographic showing various Dbt Data Engineer job openings in the United States as of June 2026, with employment types broken down into 3% As Needed, 96% Full Time, and 1% Part Time. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $129,716 per year, or $62.4 per hour.
Population Health Data Engineer

Population Health Data Engineer

Software Technology Inc

Boise, ID • On-site

$109K - $130K/yr

Other

Posted 12 days ago

Job description

Population Health Data Engineer

We are seeking a skilled Population Health Data Engineer with deep expertise in Epic data ecosystems and healthcare analytics. This role will focus on designing, building, and optimizing data pipelines and models to support population health, quality of care and claims analytics.

Key Responsibilities
  • Design, develop, and maintain scalable data pipelines supporting population health, claims analytics, and reporting.
  • Work extensively with Epic data sources including Registries, Rosters, Chronicles, Clarity, and Caboodle.
  • Integrate clinical and claims data to support longitudinal patient views and advanced analytics.
  • Develop data models for population health use cases including quality measures, risk stratification, utilization, and care management analysis.
  • Support development and operationalization of risk scoring data models and analytics (e.g., MARA, HCC, RAF).
  • Process and transform healthcare claims data (medical and pharmacy) for analytics and reporting.
  • Work with Milliman MedInsight data structures to support payer-provider analytics and efficiency benchmarking.
  • Build and optimize ELT pipelines using modern cloud platforms.
  • Collaborate with healthy planet, efficiency, quality, clinical, and analytics teams to translate business needs into technical solutions.
  • Ensure data quality, governance, and compliance with healthcare regulations (e.g., HIPAA).
  • Optimize performance of large-scale datasets and queries.
Required Qualifications
  • Strong hands-on experience with Epic systems, including:
    • Epic Registries
    • Chronicles data structures
    • Hyperspace or Hyperdrive environments
    • Clarity and Caboodle data models
  • Experience with modern data engineering tools and platforms:
    • Snowflake (data warehousing)
    • DBT (data transformation and modeling)
    • Dynamic Tables in Snowflake
  • Solid understanding of healthcare domain concepts, including population health and value-based care.
  • Experience with healthcare claims processing (medical and pharmacy claims).
  • Hands-on experience with Milliman MedInsight data models and analytics workflows.
  • Strong SQL and data modeling expertise.
  • Experience building and maintaining data pipelines.
Key Skills
  • Population Health & Risk Analytics
  • Healthcare Data Modeling (Clinical and Claims)
  • Epic Data Ecosystem Expertise
  • Snowflake & DBT
  • SQL & Performance Optimization
  • Data Governance & Compliance
Education & Experience
  • Bachelor’s or Master’s degree in Computer Science, Health Informatics, Data Engineering, or related field.
  • 6+ years of experience in data engineering, with strong preference for healthcare, payer, or population health analytics experience.

Apply