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# Data Engineer

- **Company:** System One
- **Location:** Minneapolis, MN, United States
- **Contract:** Permanent contract
- **Skills:** Sql Server Data Tools (SSDT), Business Logic, Microsoft Azure, Information Engineering, Data Integrity, Extract Transform Load (ETL), Data Warehousing, Database Development, Dimensional Modeling, Microsoft SQL Server, Performance Tuning, SQL Server Integration Services, Data Streaming, Transact-SQL, Azure Data Factory, Indexer, Star Schema, Databricks
- **Published:** May 19, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=b834b260247b1480

## About the Role

* 4-7 Years of Experience in a Data Engineering or SQL Development role.
 * Expert T-SQL: You must be "fluent" in window functions, CTEs, MERGE statements, and complex procedural logic.
 * Architecture Knowledge: Demonstrated experience with the Kimball Methodology and the Medallion (Bronze/Silver/Gold) data design pattern.
 * Tooling: Experience with MS-SQL Server stack (SSIS/SSDT) or Azure-native tools (Azure Data Factory, Synapse, or Databricks).
 * Data Integrity: A "perfectionist" mindset regarding data reconciliation and lineage.

## Description

Job Summary: System one is seeking a Data Engineer in Minneapolis, MN to help lead the development of our enterprise data warehouse. You will be the primary engine behind our Medallion Architecture, responsible for the transformation of data from Raw to Delta to Conformed to Star Schema., * End-to-End Pipeline Ownership: Build and optimize high-throughput ETL/ELT workflows using MS-SQL Server, ensuring seamless data flow across all architectural layers.

 * SCD Type 2 Mastery: Design and implement Slowly Changing Dimensions (Type 2) to maintain rigorous historical accuracy. You will manage surrogate keys, effective/expiration dates, and "is current" flagging logic.
 * Dimensional Modeling: Transform conformed business logic into high-performance Star Schemas. You should be an expert at identifying Grain, defining Facts vs. Dimensions, and handling bridge tables or junk dimensions when necessary.
 * Data Quality & Auditing: Implement standard "Data Warehouse DNA" across all tables, including Sentinel Records (-1 rows) for referential integrity and standardized InsertDate / UpdateDate audit columns.
 * Performance Engineering: Conduct deep-dive performance tuning. You should be comfortable analyzing execution plans, optimizing joins, and implementing partitioning or columnstore indexing to handle scale.


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