> Markdown version of [/jobs/ext/2922225-sr-data-engineer-databricks-sql-adf](https://www.wearedevelopers.com/jobs/ext/2922225-sr-data-engineer-databricks-sql-adf). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Sr. Data Engineer - Databricks / SQL / ADF - **Company:** Keypixel Software Solutions - **Location:** Eden Prairie, MN, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Query Performance, Microsoft Azure, Information Engineering, Extract Transform Load (ETL), Data Transformation, Data Warehousing, Dimensional Modeling, Microsoft SQL Server, SQL Azure, Standard Sql, SQL Databases, Transact-SQL, Apache Spark, Pyspark, Data Analytics, Databricks - **Published:** September 15, 2026 - **Apply:** https://www.dice.com/job-detail/dd1e34ae-e016-4355-811e-cd61e27d8814 ## About the Role We are seeking a highly skilled Senior Data Engineer with expertise in Databricks, SQL, and ADF to join our team in Eden Prairie, MN. The ideal candidate will have 12+ years of experience in data engineering and a strong background in data warehousing and optimization., * Experience with SQL Server, Azure SQL, and advanced T-SQL * Proficiency in Databricks, Apache Spark, PySpark, and ADF * Knowledge of data warehousing concepts and dimensional modeling * Hands-on experience with SCD (Slowly Changing Dimensions) and ETL/ELT processes * Familiarity with Azure Cloud services * Ability to tune queries and optimize performance ## Description * Develop and maintain data pipelines using Databricks, Apache Spark, and SQL * Design and implement ETL/ELT processes for data transformation and loading * Work closely with stakeholders to understand data requirements and deliver solutions * Optimize query performance and ensure data quality and integrity * Collaborate with cross-functional teams to support data analytics and reporting * Stay current on industry trends and best practices in data engineering