dbt Engineer

Responsibilities

• dbt Development and Best Practices
• Build, develop, and maintain a robust dbt project using dbt Cloud, ensuring alignment with a layered approach (e.g., staging, intermediate, marts).
• Data Pipeline Implementation
• Build and maintain optimized ELT/ETL pipelines within Snowflake using dbt, focusing on efficient SQL transformations, materializations (tables, views, incremental models), and performance tuning.
• Automation and CI/CD
• Implement and manage CI/CD pipelines for dbt using dbt Cloud's native features or integrations with tools like GitHub Actions, ensuring that code changes are automatically tested and deployed.
• Testing and Data Quality
• Develop and implement a comprehensive testing strategy using dbt's built-in and custom tests to ensure data quality, accuracy, and integrity.
• Leverage external integrations and dbt packages for advanced testing, data observability (e.g., Metaplane), and data quality monitoring.
• Data Lineage and Governance
• Apply dbt's data lineage features to create a transparent, end-to-end view of data flow from source to final models, visualizing dependencies via the Directed Acyclic Graph (DAG).
• Enrich models with metadata and documentation using schema.yml to improve data discovery and governance.
• Collaboration and Mentorship
• Collaborate with data analysts, data scientists, and business partners to translate business requirements into technical dbt models.
• Provide technical leadership, participate in code reviews, and mentor other team members on dbt methodologies and engineering standard processes.
• Performance Optimization
• Actively monitor and optimize dbt project performance in Snowflake, including managing costs, optimizing query execution, and selecting the most appropriate materialization strategy.
• Advanced Features and Ecosystem
• Stay current with new dbt features (e.g., Semantic Layer, column-level lineage) and leverage other tools in the modern data stack to enhance dbt capabilities.

Requirements

• Leveraging AI-powered assistants is highly desired. (e.g., Claude Code, dbt Copilot, Cursor) to automate routine tasks, including generating documentation, authoring data quality tests, and scaffolding dbt models.
• Implementing AI-enhanced workflows is highly desired. (To streamline development, using intelligent code suggestions and state-aware orchestration to reduce manual refactoring and increase developer velocity, etc)
• dbt Certification (e.g., dbt Analytics Engineering Certification)

Benefits

Additional Information

We're seeking out a dbt Engineer as an expert in data transformation and analytics engineering, responsible for leading the design, implementation, and optimization of our data pipelines using dbt (Data Build Tool) Cloud in a Snowflake environment. This role requires deep knowledge of dbt guidelines, ecosystem tooling, and continuous integration and delivery pipelines to deliver high-quality, trusted data models for analytics, reporting, and downstream applications. You will be instrumental in building a modern data platform that is scalable, reliable, and well-documented. Required Qualifications Location: this position has preference to based in hybrid work location (onsite at Meridian Idaho campus and WFH). There may be opportunity for fully remote within a mutually acceptable location. Education: Bachelor's degree in quantitative or related technical field, or equivalent practical experience (two years' relevant work experience is equivalent to one-year college). Experience: 2-4/+ in data engineering or analytics engineering, preferably in healthcare. Experience should also span: 2-3+ years of extensive experience with dbt, including advanced concepts like Jinja macros, packages, and custom tests. Hands-on experience with dbt Cloud, including project setup, environment management, and job orchestration. Proven experience working with a cloud data warehouse, preferably Snowflake. Expertise in writing and optimizing complex SQL queries. Strong understanding of data modeling principles (e.g., Kimball, Data Vault). Experience with Python scripting for data-related tasks and automation. Familiarity with version control (Git) and CI/CD practices. Excellent communication, problem-solving, and collaboration skills.

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