We are looking for a Data Quality Engineer to build ETL/ELT solutions and DWH/DB systems at scale. You will create Python- and SQL-based test automation, validate DWH/Data Lake pipelines, and embed checks into CI/CD workflows across cloud data services.

Responsibilities

Design automated test scripts to validate ETL workflows and data pipeline behavior
Execute end-to-end data validation, reconciliation, and DWH/DB testing across large datasets
Collaborate with data engineers, analysts, and stakeholders to align test strategies with data quality standards
Build reusable, scalable test automation frameworks using Python and SQL
Integrate automated tests into CI/CD pipelines to enable continuous testing and delivery
Validate data transformation logic and completeness across Azure, AWS, and GCP platforms
Contribute to Agile ceremonies, including sprint planning, retrospectives, and story grooming

Requirements

Professional experience of 4-6 years in data quality engineering, including ETL automation and Data Warehouse/Data Lake testing
Strong command of SQL for data validation, reconciliation, and test automation routines
Hands-on knowledge of Python for scripting, utilities, and framework development
Practical experience with one or more Test Automation Frameworks
Working exposure to Cloud Data Services across Azure (ADF, ADB), AWS (Glue, Lambda, Redshift), and GCP (BigQuery)
Solid understanding of CI/CD tools and practices, including Jenkins, Git, and Azure DevOps
Familiarity with Agile/Scrum methodologies and iterative delivery

Nice to have

Experience with data observability or data quality tools such as Great Expectations or Deequ
Basic knowledge of data governance and metadata management concepts
Cloud certifications in Azure, AWS, or GCP