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Dbt Developer Jobs in Michigan (NOW HIRING)

Stellantis

Senior Data Engineer (Foundations)

Auburn Hills, MI · On-site

$100.30K - $136.30K/yr

... developer tooling consumed by multiple teams • Python: idiomatic, testable, production-grade code -- not just scripting • dbt-core: advanced modelling (custom materializations), testing ...

Deloitte

Forward Deployed Engineer- AWS

Detroit, MI

Data engineering experience with Spark, Airflow/dbt, streaming, data modeling or ML/data science background feature engineering, experimentation or model evaluation * Experience with MLOps/LLMOps ...

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How much do dbt developer jobs pay per hour?

As of May 30, 2026, the average hourly pay for dbt developer in Michigan is $46.05, according to ZipRecruiter salary data. Most workers in this role earn between $35.19 and $56.35 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a DBT Developer, and why are they important?

To thrive as a DBT Developer, you need strong SQL expertise, data modeling skills, and experience with ETL processes, typically supported by a background in computer science or data engineering. Familiarity with DBT (Data Build Tool), version control systems like Git, and cloud data warehouses such as Snowflake or BigQuery is essential. Attention to detail, problem-solving abilities, and effective communication help you deliver scalable data solutions and collaborate with cross-functional teams. These skills are crucial for building reliable data pipelines, ensuring data quality, and enabling data-driven decision-making within organizations.

How does a DBT Developer typically collaborate with data engineers and analysts on a project?

DBT Developers frequently work alongside data engineers to ensure that data pipelines provide clean, reliable data to downstream users. They collaborate with analysts to understand data requirements, define business logic, and implement transformations that support analytics and reporting. Regular communication is essential for aligning on naming conventions, documenting models, and troubleshooting issues. This collaboration often takes place through code reviews, shared documentation, and agile ceremonies such as sprint planning or stand-ups.

What is a Dbt Developer?

A Dbt Developer is a data professional who specializes in using dbt (data build tool) to transform raw data into clean, reliable datasets for analytics and business intelligence. They write modular SQL code to perform data transformations, manage data models, and ensure data quality within modern data warehouses. Dbt Developers collaborate closely with data engineers, analysts, and business users to create efficient, maintainable data workflows. Their work enables organizations to make informed decisions based on trustworthy and well-structured data.

What is the difference between Dbt Developer vs Data Engineer?

AspectDbt DeveloperData Engineer
Primary FocusBuilding and maintaining data transformation pipelines using dbtDesigning, developing, and managing data infrastructure and pipelines
Skills & CertificationsSQL, dbt, data modeling, analytics skillsSQL, Python, ETL tools, cloud platforms, data architecture
Work EnvironmentAnalytics teams, data warehouses, BI projectsData platforms, cloud environments, big data systems

While both roles involve working with data, a Dbt Developer specializes in transforming data using dbt within analytics and BI projects, whereas a Data Engineer focuses on building and maintaining the broader data infrastructure and pipelines across various systems.

What are popular job titles related to Dbt Developer jobs in Michigan? For Dbt Developer jobs in Michigan, the most frequently searched job titles are:
What job categories do people searching Dbt Developer jobs in Michigan look for? The top searched job categories for Dbt Developer jobs in Michigan are:
What cities in Michigan are hiring for Dbt Developer jobs? Cities in Michigan with the most Dbt Developer job openings:
Dbt Developer jobs near you

Healthcare Data Engineer (Brighton MI Office)

CommonSail Investment Group

Brighton, MI • On-site

$109.90K - $131.90K/yr

Full-time

Posted 13 days ago

Job description

Data Engineer
CommonSail Investment Group
Location: Brighton, Michigan
Overview: As a Data Engineer, you will design, build, and maintain the data infrastructure within CommonSail Investment Group. You will work at the intersection of senior housing data, healthcare operations data and modern data engineering - building robust pipelines, maintaining our Snowflake cloud data lake, developing APIs, and partnering with IT and data professionals to ensure the right data reaches the right people at the right time. This role is pivotal in ensuring that CommonSail's data assets are accurate, secure, accessible, and leveraged effectively to support strategic business initiatives and operational excellence.
Qualifications:
  • Bachelor's degree in Information Systems, Computer Science, Data Science, or a related field.
  • 3 - 7 years of experience in data engineering, data infrastructure, API development, data integration, and data governance.
  • Strong command of SQL and Python for data transformation, automation, and pipeline development
  • Demonstrated hands-on experience with Snowflake, including performance tuning, clustering, and access control.
  • Proficiency with dbt (data build tool) for transformation layer development, testing, and documentation.
  • Experience building and consuming RESTful APIs, with working knowledge of authentication patterns (OAuth 2.0, API keys).
  • Knowledge of data privacy, compliance, and security standards (e.g., HIPAA, GDPR).
  • Strong communication, problem-solving, and analytical skills.
  • Experience in healthcare, long-term care, or related regulated industries - familiarity with PointClickCare, MatrixCare, or similar EHR/EHR-adjacent systems is a strong plus.

Primary Responsibilities:
  • Data Pipelines: Design, build, and maintain scalable data pipelines for ingestion, transformation, and delivery of healthcare operations data from multiple client systems and third-party sources.
  • Data Architecture & Administration: Architect and administer the organization's Snowflake data warehouse, including database design, role-based access control, query optimization, and cost governance.
  • DBT: Develop and maintain dbt models for data transformation, testing, documentation, and lineage across all data domains (census, financials, clinical, acquisition targets).
  • API Development: Build and maintain RESTful APIs and integration services that connect source systems, internal tools, and analytical platforms.
  • Data Lake Administration: Design and implement data lake strategies for raw data storage, archival, and cost-efficient processing of high-volume datasets.
  • Data Modeling: Create and enforce data modeling standards - dimensional modeling, star/snowflake schemas, and normalized models - across the enterprise data warehouse.
  • Stakeholder Collaboration: Partner with business leaders and technical teams to align data strategy with organizational goals, translating requirements into scalable solutions.
  • Performance Monitoring: Monitor pipeline health, establish alerting and data quality checks, and resolve incidents with urgency and rigor.
  • Data Support: Support business operation engagements with data room analysis, source system evaluation, and integration planning for EHR entities.
  • Data Engineering Practices: Champion data governance, documentation, and best practices across the data engineering function.

Skills:
  • Cloud Data Warehousing (Snowflake): Design, administer, and optimize cloud-based data warehouses, including schema design, performance tuning, cost governance, security, and role-based access control.
  • Data Transformation & Modeling: Develop scalable transformation layers using dbt and SQL, implementing dimensional and normalized models to support analytics and downstream consumption.
  • Programming & Analytics: Use Python and SQL for data transformation, automation, pipeline logic, and analytical problem-solving across batch and event-driven workloads.
  • Data Pipelines & Orchestration (ETL / ELT, Airflow or Similar): Build, schedule, and monitor ETL/ELT pipelines with orchestration tools to ensure timely, accurate, and resilient data processing.
  • Data Ingestion & Integration (Fivetran, Airbyte, APIs): Ingest data from SaaS platforms, databases, and systems using managed connectors and custom integrations, ensuring scalability and data reliability.
  • API Development & Data Services (REST APIs, JSON): Design and consume RESTful APIs to exchange data between systems, using standard formats such as JSON and secure authentication patterns.
  • Cloud Platforms & Supporting Technologies (AWS, Azure, GCP): Leverage major cloud platforms and supporting services for storage, compute, networking, and security to build flexible and scalable data solutions.
  • Data Storage Formats & Lakes (Data Lakes, Parquet, Avro): Implement data lake architectures using efficient, columnar storage formats to support large-scale analytics, archival, and cost-effective querying.
  • Development Practices & Tooling (Git, VS Code, Database Administration): Apply modern development workflows using version control, IDEs, and database administration best practices to maintain high-quality, well-documented data systems.

General Working Conditions: While performing the duties of this job, the employee is required to communicate effectively with others, sit, stand, walk, and use their hands to handle the keyboard, telephone, paper, files, and other equipment and objects. The employee is occasionally required to reach with hands and arms. This position requires the ability to review detailed documents and read computer screens. The employee will occasionally lift and/or move up to 25 pounds. The work environment requires appropriate interaction with others. The noise level in the work environment is moderate. Occasional travel to different locations may be required.
Equal Opportunity Employer
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