Overview
The Senior Full Stack Data Quality Engineer will provide specialized data quality engineering services to the Finance & Data Reporting Product Team. This role focuses on validating data across the full data lifecycle, including various processes and platforms, while ensuring high standards of data quality through testing and automation. The successful candidate will collaborate with engineering and business stakeholders to deliver operational assurance and initiative outcomes in a modern data environment.
Responsibilities
- Design and execute data quality testing across various layers of data processing.
- Validate data outputs against source systems, business rules, and reporting requirements.
- Test ETL and ELT processes, dbt models, transformations, and Power BI outputs.
- Apply comprehensive data quality checks including accuracy, completeness, and validity.
- Collaborate with stakeholders to resolve data quality issues through root cause analysis.
- Maintain and improve the dbt test harness for Snowflake data validation.
- Integrate automated tests into continuous integration and deployment workflows.
- Provide concise reporting on testing progress, defects, and data quality risks.
Requirements
- Proven experience in full-stack data quality engineering, particularly in data testing and validation.
- Strong knowledge of Snowflake, dbt, SQL, and Power BI.
- Experience with automation frameworks using Python, GitHub, and Azure DevOps.
- Demonstrated ability to perform root cause analysis of data defects.
- Familiarity with CI/CD processes and integration of automated tests.
- Understanding of legacy and modern data platforms, including SQL Data Warehouse and Snowflake.
- Ability to translate business rules into testable validation logic.