Data QA Engineer

Were looking for a Data QA Engineer with hands-on experience validating modern data platforms that ingest, transform, and operationalize large datasets. This role focuses on ensuring data quality, reliability, and correctness across end-to-end pipelines built on AWS and dbt.
Key Responsibilities• Design, develop, and execute manual and automated test cases for data ingestion, transformation, and consumption layers.• Validate data completeness, consistency, and accuracy across diverse source and target systems.• Write advanced SQL queries and supporting scripts to perform data profiling, reconciliation, anomaly detection, and regression checks.• Review data pipeline designs with data engineers to define test strategy, coverage, and potential failure points.• Build and maintain automated data quality checks within dbt (schema tests, custom tests, documentation validation).• Partner with engineering teams to triage data defects, drive root-cause analyses, and track remediation to closure.• Contribute to improving data QA frameworks, processes, and CI/CD integration for continuous testing.• Support testing across AWS components such as Redshift, Glue, EMR, S3, Lambda, and related platform services.Required Skills & Experience• Strong proficiency in SQL with the ability to write complex joins, aggregations, and validation queries.• Hands-on experience testing ETL/ELT pipelines and a solid understanding of data warehousing concepts.• Practical experience with dbt (tests, models, lineage, CI workflows).• Experience with scripting languages such as Python or Bash for test automation and data validation tasks.• Familiarity with AWS data ecosystem (Redshift, Glue, EMR, S3, Athena, Lambda).• Exposure to data testing or automation tools (e.g., Great Expectations, Soda, Deequ, custom frameworks).