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# Databricks SQL and Analytics Engineer

- **Company:** ConsultNet
- **Location:** South Jordan, UT, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Query Performance, Data Analysis, Business Intelligence Development, Data Validation, Information Engineering, Data Infrastructure, Dimensional Modeling, Python (Programming Language), Query Optimization, Power BI, Standard Sql, SQL Databases, Tableau (Software), Sql Optimization, Caching, Git, Looker Analytics, Databricks
- **Published:** November 7, 2026
- **Apply:** https://www.thejobnetwork.com/job/6848f207-664f-4e07-9293-52b164ed320f/databricks-sql-and-analytics-engineer

## About the Role

* 4 years in analytics engineering, business intelligence development, or data engineering

 * Advanced SQL including window functions, complex joins, and query optimization

 * Hands on experience with a modern BI platform such as Power BI, Tableau, or Looker

 * Understanding of dimensional modeling and how to design for consumption

 * Version controlled development practice using Git

 * Strong stakeholder communication and requirements gathering skills

Preferred Skills

 * Databricks Certified Data Analyst Associate

 * Experience with dbt on Databricks

 * Working knowledge of Unity Catalog governance as it applies to analytics consumers

 * Python for automation and lightweight transformation work

 * Experience with semantic layer tooling and metric stores

## Description

The Databricks SQL and Analytics Engineer sits between the data platform and the people asking questions of it. This role builds the curated gold layer models, the metrics definitions, and the dashboards that business teams rely on, and makes sure the numbers hold up when someone checks them. It is the right role for a strong SQL practitioner who cares about how the business actually uses the output., * Build and maintain curated analytics models and gold layer tables using SQL and Databricks SQL

 * Define and document metrics so the same question returns the same answer regardless of who asks

 * Develop dashboards and self service datasets in Databricks SQL, Power BI, or Tableau

 * Tune SQL warehouse sizing, caching, and query performance for interactive workloads

 * Implement testing and data quality checks on analytics models

 * Partner with business stakeholders on requirements and translate ambiguous asks into specific definitions

 * Enable self service consumers through documentation, training, and sensible dataset design