nuArch
Summary
The Data Warehouse Developer is responsible for the technical implementation of enterprise data warehouse and data mart solutions. This includes data modeling, ETL (Extract, Transform, Load) processes, system integration, and performance optimization to ensure the delivery of scalable and reliable data infrastructure supporting analytics and reporting functions. Key Responsibilities
Design, build, and optimize data warehouse and data mart structures, including star and snowflake schemas. Develop robust and scalable ETL pipelines to integrate data from multiple source systems, including DOE systems. Implement data quality, cleansing, validation, and auditing procedures to ensure accuracy and integrity. Ensure robust security, access control, and compliance with DOE and organizational data governance standards. Collaborate closely with Data/Business Analysts to translate business requirements into technical specifications. Support database performance tuning, historical data loading, schema versioning, and optimization strategies. Provide clear and maintainable technical documentation for ongoing development, operations, and support. Required Qualifications
Bachelor's degree in Computer Science, Information Systems, or related field. 5+ years of hands-on experience developing and maintaining enterprise data warehouses. Strong proficiency in SQL and data modeling techniques (e.g., dimensional modeling, normalization). Experience with ETL development tools (e.g., SSIS, Azure Data Factory, Informatica, etc.). Familiarity with modern data warehouse platforms such as Microsoft SQL Server, Snowflake, or Azure Synapse. Knowledge of data governance, metadata management, and data quality frameworks. Preferred Skills
Experience with Microsoft Fabric or similar cloud data platforms. Knowledge of Power BI or other reporting/visualization tools. Understanding of iterative development methodologies. Strong problem-solving and communication skills. Engagement Level
Full-time engagement throughout the project lifecycle. Higher intensity expected during data integration, modeling, and go-live phases.
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The Data Warehouse Developer is responsible for the technical implementation of enterprise data warehouse and data mart solutions. This includes data modeling, ETL (Extract, Transform, Load) processes, system integration, and performance optimization to ensure the delivery of scalable and reliable data infrastructure supporting analytics and reporting functions. Key Responsibilities
Design, build, and optimize data warehouse and data mart structures, including star and snowflake schemas. Develop robust and scalable ETL pipelines to integrate data from multiple source systems, including DOE systems. Implement data quality, cleansing, validation, and auditing procedures to ensure accuracy and integrity. Ensure robust security, access control, and compliance with DOE and organizational data governance standards. Collaborate closely with Data/Business Analysts to translate business requirements into technical specifications. Support database performance tuning, historical data loading, schema versioning, and optimization strategies. Provide clear and maintainable technical documentation for ongoing development, operations, and support. Required Qualifications
Bachelor's degree in Computer Science, Information Systems, or related field. 5+ years of hands-on experience developing and maintaining enterprise data warehouses. Strong proficiency in SQL and data modeling techniques (e.g., dimensional modeling, normalization). Experience with ETL development tools (e.g., SSIS, Azure Data Factory, Informatica, etc.). Familiarity with modern data warehouse platforms such as Microsoft SQL Server, Snowflake, or Azure Synapse. Knowledge of data governance, metadata management, and data quality frameworks. Preferred Skills
Experience with Microsoft Fabric or similar cloud data platforms. Knowledge of Power BI or other reporting/visualization tools. Understanding of iterative development methodologies. Strong problem-solving and communication skills. Engagement Level
Full-time engagement throughout the project lifecycle. Higher intensity expected during data integration, modeling, and go-live phases.
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