Job SummaryThe Data Warehouse Quality Engineer will support the ongoing development and maintenance of our data warehousing systems and data marts. Through your efforts, you'll be making valuable contributions to the continuous transformation and improvement of our information systems.
This role works with the rest of the Data Warehouse team to support and test throughout the entire data development lifecycle, including data profiling, ETL and data model design, development, testing and support. We're looking for someone who has experience testing data warehouse systems and ETL, is team oriented, quality-focused and a great communicator, with the skills necessary to ensure successful growth and support of our data warehousing systems.
Responsibilities - Collaborate with project teams and business users to gather requirements and develop effective test plans, test cases, and technical scripts
- Participate in Agile/Scrum ceremonies and provide clear QE status updates
- Test ETL pipelines, DBT transformations, and Snowflake data loads to ensure data accuracy and integrity
- Validate source-to-target mappings by writing Snowflake SQL queries to reconcile data from different sources like SQL Server, files, and AWS S3
- Analyze and profile data to identify quality issues and assess ETL readiness
- Develop and maintain automated scripts for data validation and regression testing in Python
- Document test results, defects, and resolutions, and communicate findings to stakeholders
Minimum Qualifications- A bachelor's degree and 3 years of professional work experience (or a master's degree, or equivalent experience) is required.
Additional Qualifications - 3+ years of experience in IT software development (such as Systems Analysis, Data Warehousing, ETL, and QE areas)
- Knowledge of data warehousing concepts
- Experience with cloud data warehouse technologies such as AWS, DBT, Snowflake.
- Strong SQL skills for data validation and analysis (experience with SQL Server and Snowflake).
- Familiarity with DBT (Data Build Tool) for data transformation validation.
- Experience working with relational and dimensional data models
- Must be a self-starter comfortable working in a fast-paced, flexible environment, and take the initiative to learn new tools and concepts quickly
