Job
Description
As a Data Warehouse Developer, your role involves designing, developing, and optimizing SQL queries for data extraction, transformation, and reporting within large-scale data warehouse environments. You will translate business needs into efficient SQL queries and data models to support analytics and decision-making. Key Responsibilities: Develop complex SQL queries for ETL processes within the data warehouse and document SQL development standards and procedures. Design and optimize SQL scripts to improve data retrieval performance and data quality. Implement and maintain dimensional data models using SQL (star schema, snowflake schema). Write stored procedures, functions, views, and triggers to automate data workflows. Collaborate with BI teams and business stakeholders to gather reporting requirements and optimize existing SQL queries for better performance. Perform data profiling and cleansing using SQL to ensure high-quality data. Troubleshoot and debug SQL code related to data warehousing processes. Manage Slowly Changing Dimensions (SCD) for historical data changes tracking. Ensure data quality and consistency by implementing validation rules and data cleansing procedures. Develop and maintain metadata and documentation for data warehouse architecture, schemas, and ETL processes. Qualifications: Strong SQL skills including complex queries, joins, stored procedures, views, and performance tuning. Solid understanding of Data Warehouse concepts such as fact/dimension tables, SCD, and schema design. Experience with AWS data services like Redshift, Glue, S3, Lambda, and RDS/Aurora. Familiarity with cloud architecture, security best practices on AWS, and ETL development in AWS environments. Good to Have Skills: Design and develop dimensional and normalized data models efficiently. Understand differences between OLTP and OLAP systems, design solutions for analytical workloads. Apply data governance and security best practices to protect sensitive data. Use SQL extensively for data extraction, transformation, and analysis in the data warehouse. If you have any further questions or need additional details, feel free to ask. As a Data Warehouse Developer, your role involves designing, developing, and optimizing SQL queries for data extraction, transformation, and reporting within large-scale data warehouse environments. You will translate business needs into efficient SQL queries and data models to support analytics and decision-making. Key Responsibilities: Develop complex SQL queries for ETL processes within the data warehouse and document SQL development standards and procedures. Design and optimize SQL scripts to improve data retrieval performance and data quality. Implement and maintain dimensional data models using SQL (star schema, snowflake schema). Write stored procedures, functions, views, and triggers to automate data workflows. Collaborate with BI teams and business stakeholders to gather reporting requirements and optimize existing SQL queries for better performance. Perform data profiling and cleansing using SQL to ensure high-quality data. Troubleshoot and debug SQL code related to data warehousing processes. Manage Slowly Changing Dimensions (SCD) for historical data changes tracking. Ensure data quality and consistency by implementing validation rules and data cleansing procedures. Develop and maintain metadata and documentation for data warehouse architecture, schemas, and ETL processes. Qualifications: Strong SQL skills including complex queries, joins, stored procedures, views, and performance tuning. Solid understanding of Data Warehouse concepts such as fact/dimension tables, SCD, and schema design. Experience with AWS data services like Redshift, Glue, S3, Lambda, and RDS/Aurora. Familiarity with cloud architecture, security best practices on AWS, and ETL development in AWS environments. Good to Have Skills: Design and develop dimensional and normalized data models efficiently. Understand differences between OLTP and OLAP systems, design solutions for analytical workloads. Apply data governance and security best practices to protect sensitive data. Use SQL extensively for data extraction, transformation, and analysis in the data warehouse. If you have any further questions or need additional details, feel free to ask.
