About the Role :

symplr is seeking a highly skilled and motivated Senior Data Quality Engineer with 7+ years of experience to execute testing initiatives for our robust Data Platform. This role is instrumental in maintaining the integrity, reliability, and performance of our data systems.

Duties & Responsibilities :

- ETL Testing: Design and perform thorough ETL tests, including test case development, data validation, transformation accuracy, end-to-end pipeline testing, and performance testing.

- AWS Data Pipeline Monitoring: Support the monitoring and optimization of AWS-based pipelines using services like Glue, S3, Athena, MSK, SQS, Lambda, Step Functions etc to ensure data accuracy, completeness, and reliability.

- Automation & Scripting: Develop Python-based scripts to automate data quality checks and validation processes.

- Power BI Reports: Ensure data accuracy and consistency in Power BI reports by validating data lineage, transformation logic, and report outputs against source systems.

- Collaboration: Work alongside data engineers, analysts, and business stakeholders to understand data flows and resolve data quality issues.

- Documentation: Maintain clear and detailed documentation of testing procedures, test results, and quality metrics.

Skills Required :

- 5+ years of experience in data quality engineering, data testing, or related fields.

- Strong hands-on experience with ETL testing and data validation techniques.

- Experience working with AWS data services, including Glue, S3, Athena, MSK, SQS, Lambda, Step Functions, etc.

- Proficiency in SQL and Python (or any other OOP languages like Java, C#, C++, etc).

- Experience validating data in Power BI reports, including understanding of data models and report logic.

- Familiarity with test management tools such as Azure DevOps, Zephyr, or equivalent.

- Experience working in Agile environments (Scrum/Kanban).

- Exposure to data governance principles or data observability tools is a plus.

- Strong problem-solving skills and a proactive approach to identifying and resolving data quality issues.

- Good communication skills and the ability to work effectively with cross-functional teams.

- Continuous learning mindset and willingness to adapt to new technologies and techniques.