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symplr • Karnataka, India
Role & seniority: Senior Data Quality Engineer (7+ years of experience)
Stack/tools: AWS data services (Glue, S3, Athena, MSK, SQS, Lambda, Step Functions), ETL testing, data validation, Power BI, Python, SQL, test management tools (Azure DevOps, Zephyr), Agile (Scrum/Kanban)
Design and execute data quality strategies, including ETL test cases, data validation, transformation accuracy, end-to-end and performance testing
Monitor and optimize AWS-based data pipelines to ensure data accuracy, completeness, and reliability
Develop Python-based automation for data quality checks and validate Power BI data lineage and report outputs
5–7+ years in data quality/testing or related field
Strong ETL testing and data validation experience
Experience with AWS data services (Glue, S3, Athena, MSK, SQS, Lambda, Step Functions)
Proficiency in SQL and Python (or other OOP languages)
Experience validating data in Power BI reports and understanding data models
Familiarity with test management tools; Agile experience
Good problem-solving, cross-functional collaboration, and strong communication
Exposure to data governance principles or data observability tools
Continuous learning mindset and ability to adapt to new technologies
Location & work type: Location and work type not specified in the description
Overview 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. The ideal candidate will bring strong expertise in ETL testing, data validation, AWS-based data workflows, and Power BI reports, with a solid foundation in Test Automation and scripting. Duties & Responsibilities Contribute to the development and execution of data quality strategies and best practices to support the organization’s data initiatives.
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.