Data Analytics Engineer (Snowflake + DBT+ Power BI) - W2

The Analytics Engineer acts as a bridge between data engineers and data analysts, with a primary focus on refactoring existing data models and designing analyticsready fact and dimension tables to support the migration of reports and dashboards to Power BI. This role is responsible for transforming existing analytical datasets into curated, reusable, and trusted dimensional models that serve as the foundation for enterprise reporting and selfservice analytics. The Analytics Engineer will evaluate currentstate models, recommend appropriate dimensional structures, and implement highquality fact and dimension tables in Snowflake using dbt and SQL. The role partners closely with analytics engineers, data analysts, and BI developers to validate model design, metrics, and assumptions, ensuring analytical accuracy and performance. In addition to model development, this role supports data quality, testing, documentation, and performance optimization, with an emphasis on enabling reliable Power BI semantic models and dashboards. The Analytics Engineer works independently within defined scope, contributes to Agile delivery, and ensures effective knowledge transfer at the conclusion of the contract.

Essential Functions

• Collaborate with analytics engineers, data analysts, and business stakeholders to assess existing data models and identify opportunities for fact and dimension refactoring in support of Power BI migration
• Design and implement dimensional data models (facts, dimensions, conformed dimensions) aligned with best practices and reporting requirements
• Recommend the most appropriate dimensional modeling approach or consolidation based on report usage patterns, grain, and analytical needs
• Develop and maintain analytics engineering pipelines in Snowflake using dbt, SQL, and established modeling standards
• Refactor and extend existing data models to improve clarity, reuse, performance, and analytical correctness
• Partner with analysts and BI developers to validate metrics, relationships, and grain, ensuring models accurately support downstream reporting
• Support Power BI semantic models by aligning table structures, relationships, and metric definitions with visualization and performance requirements
• Implement data quality checks, validation logic, and testing frameworks to ensure trusted and reliable datasets
• Optimize SQL transformations and model structures to improve performance and manage warehouse compute usage
• Document dimensional models, design decisions, assumptions, and usage guidance to support adoption and longterm sustainability
• Participate in Agile or iterative delivery workflows and effectively manage time across multiple priorities and deliverables
• Collaborate with data engineers and architects as needed to resolve upstream data issues and align modeling approaches
• Utilize modern analytics development practices, including version control, code review, and documentation
• Support knowledge transfer and handoff to internal team members to ensure continuity beyond the contract period.

Skills / Experience

• Advanced SQL for analytics engineering, including complex transformations, aggregations, and performance tuning.
• Strong dimensional data modeling skills, including design and implementation of fact and dimension tables
• Hands-on experience with Snowflake as a Cloud data warehouse for analytics workloads
• Experience developing and maintaining analytics models using dbt, including testing and documentation
• Proven ability to refactor existing analytical data models to support reporting and dashboard migrations
• Experience supporting enterprise BI platforms, preferably Power BI, including semantic model alignment
• Strong analytical and problemsolving skills, with the ability to evaluate trade-offs and recommend optimal modeling approaches
• Ability to collaborate effectively with analytics engineers, data analysts, and BI developers to validate models and metrics
• Strong written and verbal communication skills to explain modeling decisions, assumptions, and results.

Preferred / NicetoHave Skills

• Experience working with data sourced from EMR systems (ex. Epic)
• Experience with Apache Airflow or similar workflow orchestration tools for scheduling and managing analytics pipelines
• Experience using Python for data transformation, validation, automation, or analytics workflows
• Experience with version control and modern analytics development practices (e.g., Git, pull requests, code reviews)
• Experience supporting BI platform migrations (e.g., Qlik, Business Objects, or similar to Power BI)
• Familiarity with Agile or iterative delivery practices