Data Warehouse Engineer

About the Project
Our client operates community and specialty hospitals, a managed care organization, a physician network, community health centers, and home care and other health-related entities in the United States. The Research Portal — one of the internal products — is a web-based application with over 10,000 current users developed for the entire research community to review, monitor, and manage research portfolio. The main goal is to unite distributed and complex organizational processes into one application and help alleviate administrative burdens. 
 

Skills & Experience

• 5+ years of professional experience in data warehouse development.
• Strong hands-on experience with: T-SQL, stored procedures, query performance tuning, and indexing strategies.
• Proven experience building and maintaining ETL pipelines with SSIS (SQL Server Integration Services).
• Solid understanding of dimensional modeling (star/snowflake schemas), slowly changing dimensions, and data warehouse design best practices.
• Experience with source-to-target mapping, data quality checks, and reconciliation processes.
• English — Upper Intermediate or above.
   

Nice to have

• Familiarity with Azure data services (Azure SQL MI, IR, Data Factory, SSIS, Storage Account, Key Vault) as a migration/modernization path.
• Experience working with healthcare or other regulated/compliance-sensitive data.
• Exposure to CI/CD practices for database and ETL deployments.
   

Responsibilities

• Design, build, and maintain ETL/ELT pipelines using SSIS to move and transform data from operational systems into the data warehouse.
• Design and evolve dimensional models (facts, dimensions, SCDs) that support reporting and analytics needs.
• Write and tune complex T-SQL queries, stored procedures, and views; troubleshoot performance bottlenecks.
• Establish and enforce data quality, validation, and reconciliation processes across the warehouse.
• Collaborate closely with Product Owners, Business Analysts, and backend engineers to translate business requirements into data models and pipelines.
• Document data flows, schemas, and transformation logic to support maintainability and onboarding.
• Serve as an escalation point for data-related production issues; perform root-cause analysis and implement long-term fixes.
• Monitor ETL job health and proactively address failures, latency, and data drift.